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26th International Conference on Industrial Technology, ICIT 2025

作者： Ma, Tianyu Du, Sheng Fan, Haipeng Huang, Cheng 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

ISBN: (纸本)9798331521950

Geological drilling process, owing to complex geological environment and harsh downhole conditions, generates data including characteristics such as pressure, rotational speed, and depth, which are frequently high-dimensional and noisy. These characteristics make real-time monitoring more complex. Existing methods in the geological drilling process, such as rule-based systems and threshold techniques, struggle to handle the complexity and high dimensionality of drilling data, leading to high false alarm rates and low detection accuracy. This paper develops an integrated temporal dictionary learning with isometric mapping method for monitoring geological drilling process. Specifically, Isometric Mapping is employed to perform dimensionality reduction on high-dimensional data, thereby retaining the structural features in the lower-dimensional space. Subsequently, Lasso regularization is applied for sparse coding to extract essential features from the reduced data. To address the fluctuations arising from the iterative dictionary learning process, a temporal smoothing term is incorporated to ensure the stability of the dictionary across different time steps. After that, the reconstruction errors were adopted to achieve comprehensive statistical indicators. Then the overall monitoring was realized for the plant-wide process. The effectiveness and robustness of the proposed method are demonstrated through case studies on the Tennessee-Eastman process and the actual geothermal drilling process. © 2025 IEEE.

关键词： Mapping

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A Novel Rate of Penetration Fusion Modeling Method based on Improved Dung Beetle Optimization Algorithm and Support Vector Regression 26

A Novel Rate of Penetration Fusion Modeling Method based on ...

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26th International Conference on Industrial Technology, ICIT 2025

作者： Zhou, Yang Zhang, Zihan Chen, Shipeng Wu, Min Chen, Xin 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 School of Future Technology China University of Geosciences Wuhan430074 China

ISBN: (纸本)9798331521950

The rate of penetration (ROP) is a critical indi-cator for evaluating drilling efficiency. Developing an accurate ROP model is essential for optimizing drilling performance and addressing process control challenges. However, ROP modeling in deep geological drilling is complicated by nonlinearity, diverse working conditions, and high-dimensional variations. To overcome these challenges, a fusion modeling approach for ROP is proposed. First, the fuzzy C-means clustering method is applied to classify drilling data into different working con-ditions. Based on this classification, support vector regression is employed to develop ROP sub-models, effectively addressing nonlinearity. To further enhance model accuracy, an improved dung beetle optimization algorithm (IDBO) is designed to deter-mine optimal model parameters and integrate the sub-models, thereby resolving issues related to multiple working conditions and high-dimensional variations. The IDBO incorporates four key enhancements, average weight, chaos disturbance, modified local search, and re-updating of the best solution, to strength-en its global search capability. Comparative results using the IEEE CEC2017 benchmark test functions demonstrate that the proposed algorithm outperforms others in 12 test functions, highlighting its strong global optimization ability. Additionally, results from real-world drilling data validate the effectiveness of the proposed modeling approach in practical applications. © 2025 IEEE.

关键词： Clustering algorithms

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Hand Gesture Recognition from an Open-Set Perspective

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IEEE Transactions on Multimedia 2025年

作者： Zhou, Jun Xu, Chi Cheng, Li China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Vision and Learning Laboratory Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada

Existing hand gesture recognition methods predominantly rely on a close-set assumption, which in essence limits the viewpoints, gesture categories, and hand shapes at test time to closely resemble those seen during training. This requirement is however rarely met in practice, as images are often captured from unconstrained viewpoints, with novel gestures and unseen hand shapes that can differ significantly from the training data. This motivates us to investigate an open-set hand gesture recognition problem, where hand gestures are still recognizable from unconstrained viewpoints, and novel gesture classes and hand shapes can be incrementally learned with just a few examples. To address this, we propose a viewpoint influence elimination network that extracts view-independent features, significantly improving performance in scenarios with unconstrained viewpoints. Moreover, a joint-weighted classification scheme is introduced to augment the cosine similarity metric for evaluating few-shot incremental learning of novel gestures and shapes. Finally, as existing hand gesture recognition datasets primarily adhere to the close-set assumption, a new hand gesture recognition dataset, OHG, is introduced in this paper, that includes a wide range of viewpoints, diverse gesture classes, and distinct hand shapes. Experimental hand gesture recognition results demonstrate the superior performance of our approach in both unconstrained viewpoint and few-shot incremental learning scenarios. The source code and the OHG dataset are to be made available at https://***/zhoujun-7/OSHGR. © 1999-2012 IEEE.

关键词： Palmprint recognition

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A Stacking Ensemble Learning Method for Non-Intrusive Appliance Load Monitoring

A Stacking Ensemble Learning Method for Non-Intrusive Applia...

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Chinese control and Decision Conference, CCDC

作者： Xudong Cheng Ding Li Wenkai Hu Jinghao Xu School of Automation 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 Electrical and Information Engineering Wuhan Institute of Technology Wuhan China

ISBN: (数字)9798350387780

ISBN: (纸本)9798350387797

Non-Intrusive Load Monitoring (NILM) collects the overall power consumption information from the power panel with advanced metering infrastructure, and obtains the operating states and power consumption information of each electrical appliance through load decomposition algorithms, so as to come up with appropriate strategies to reduce energy consumption and improve energy efficiency, which is of great significance for energy conservation and reduction. In recent years, NILM methods based on deep learning algorithms have received much attention and achieved good results, but the type of features extracted by a single deep learning model is limited, resulting in the model’s inability to overcome the bottleneck of accurate decomposition. To address the above problems, this paper proposes a deep ensemble learning framework for NILM, which takes the total active power sequence as input and synthesizes the advantages of multiple deep learning neural network models to further improve the performance of appliance identification and power disaggregation. Experiments are designed and conducted on the UK-DALE dataset to verify the effectiveness of the proposed deep ensemble learning method for NILM. The experimental results show that the method proposed in this paper has more advantages and is more promising in terms of various metrics compared to single deep learning approaches.

关键词： Deep learning Training Load monitoring Power demand Accuracy Stacking Neural networks

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Anti-Sway control of Crane Based on Equivalent-Input-Disturbance Method and Feedback Linearization

Anti-Sway Control of Crane Based on Equivalent-Input-Disturb...

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Chinese control Conference (CCC)

作者： Shuhan He Gang Yu Yongbo Li Fengyu Zhang Wangyong He School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation Complex Systems Wuhan China Huaxin Cement Technology Management (Wuhan) Co. Ltd Wuhan China

The anti-sway issue with crane systems is discussed in this essay. Because cranes are undriveable and nonlinear, implementing anti-sway controllers becomes much more challenging. This work suggests a crane anti-sway controller that uses feedback linearization(FL) in conjunction with the Equivalent-Input-Disturbance (EID) technique to address the issues. to reduce the problem that the feedback linearization largely relies on the model's accuracy. The crane system is first treated as a linear system, after which the unmodeled disturbances, nonlinear components, and external disturbances of the system are treated as the total disturbances of the system, and the effects of these disturbances are then compensated for using disturbance estimation. Finally, simulation experiments confirm that the maximum steady-state fluctuations of the position and angle of the anti-sway controller based on the equivalent input disturbance method and the feedback linearization method are 14 and 6.85 percent, respectively, of those estimated without disturbances. This demonstrates the potency of this approach.

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Impulsive Formation control of Nonlinear Leader-Following Multi-agent Systems with Input Saturation*

Impulsive Formation Control of Nonlinear Leader-Following Mu...

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Industrial Cyber-Physical Systems (ICPS)

作者： Ni Zhang Xiaowei Jiang Xianhe Zhang Le You School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation of Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Key Laboratory of System Control and Information Processing Ministry of Education Shanghai China School of Electrical Engineering and Automation Hubei Normal University Huangshi China

Leader-following formation analysis problem for a second-order nonlinear multi-agent system(MAS) with input saturation is investigated in this paper. And the impulsive formation control algorithm is introduced in the designed protocol which only works at the impulse times. Owing to the real-world limited communication channels, input saturation is considered in the impulsive controller. Furthermore, based on Lyapunov stability theories, Kronecker properties, eigenvalue and so on, some sufficient conditions that guarantee the leader-following consensus of MAS are obtained. Lastly, several simulations are worked out to verify the correctness and effectiveness of the theoretical results.

关键词： Sufficient conditions Protocols Simulation Communication channels Cyber-physical systems control systems Formation control

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RGB-infrared fusion tracking algorithm based on Siamese network 2

RGB-infrared fusion tracking algorithm based on Siamese netw...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Li, Ruyou Cui, Wennan Wang, Zhiyong Zhang, Jingjing School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of Intelligent Infrared Perception Chinese Academy of Sciences 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

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

The target tracking method based on RGB image is affected by many factors such as light and haze weather, so it is difficult to distinguish the tracking target from the background, and it is easy to lead to the drift or even loss of the tracking target. Target tracking based on infrared images is not affected by light, haze and other illumination factors, but the target's color, texture and other characteristic information will be missing. Therefore, in order to obtain the target's color, texture and other characteristic information in a poorly illuminated environment, while achieving accurate and fast tracking of the object, this paper proposes a RGB and infrared image fusion tracking method based on a deep convolutional network. Firstly, the fusion method of RGB and infrared images is studied;secondly, a target tracking network based on the Siamese network is established to extract the image convolution features of the target template and the current target;finally, the response map is calculated by the deep cross-correlation module. At last, the performance test of the target tracking algorithm is carried out on the VOT2019RGBT data set. Experimental results show that the algorithm can effectively solve the problem of target tracking when the target is partially occluded, the tracking scene has no suitable illumination or the light changes strongly, and it is of great significance to improve the accuracy of target tracking under complex backgrounds. © 2021 SPIE.

关键词： Image fusion

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Speech Emotion Recognition Based on Syllable-Level Feature Extraction

SSRN

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SSRN 2023年

作者： Rehman, Abdul Liu, Zhen-Tao Wu, Min Cao, Wei-Hua Jiang, Cheng-Shan 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

Speech emotion recognition systems have high computational requirements for deep learning models and low generalizability mainly because of the poor reliability of emotional measurements across multiple corpora. To solve these problems, we present a speech emotion recognition system based on a reductionist approach of decomposing and analyzing syllable-level ***-spectrogram of an audio stream is decomposed into syllable-level components, which are then analyzed to extract statistical features. The proposed method uses formant attention, noise-gate filtering, and rolling normalization contexts to decrease contextual differences and increase focus the attention on the structure of a formant. A set of syllable-level formant features is extracted and fed into a single hidden layer neural network that makes predictions for each syllable as opposed to the conventional approach of using a sophisticated deep learner to make sentence-wide *** syllable level predictions help to lower the aggregated error in utterance level cross-corpus predictions. The experiments on IEMOCAP (IE), MSP-Improv (MI), and RAVDESS (RA) databases show that the method archives better than the state-of-the-art cross-corpus unweighted accuracy of 47.6% for IE to MI and 56.2% for MI to IE. © 2023, The Authors. All rights reserved.

关键词： Forecasting

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A Novel Ceramic Surface Based Constant Force control Method for Flatness Inspection

SSRN

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SSRN 2023年

作者： Chen, Xin Gao, Xu Zhou, Yang Qin, Lang 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

A constant force control method of the robot based on a force sensor is proposed for the difficult point of high difficulty and low accuracy of ceramic surface flatness inspection. This paper proposed a method that combines a constant impedance controller for normal contact force with a posture compliance controller. The method decomposes force control and position control orthogonally to simplify the complex three-dimensional surface model of ceramics. The method solves the problem that the end-effector is difficult to be perpendicular to the contact surface due to the change of ceramic surface shape. Moreover, the proposed method can also detect whether the ceramic surface is flat or not by the change of the force sensor's indication and the change of the actual end position of the robot arm. Finally, the defect detection experiments are conducted on a plane and on a complex surface, respectively. The experiments demonstrate the adaptability and fast convergence of the method proposed in this paper, which meets the needs of high precision and fast response of industrial processes. © 2023, The Authors. All rights reserved.

关键词： Force control

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Anomaly Detection and Alarm Limit Design for In-Hole Bit Bounce Based on Interval Augmented Mahalanobis Distance 1

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Proceedings of the 4th International Conference on Neural Computing for advanced Applications, NCAA 2023

作者： Hu, Bin Hu, Wenkai Zhang, Peng Cao, Weihua 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: (数字)9789819958474

ISBN: (纸本)9789819958467

Timely and accurate anomaly detection is of great importance for the safe operation of the drilling process. To detect bit bounce during the drilling process, this paper proposes a method based on interval augmentation Mahalanobis distance. The method first selects process variables that are closely related to bit bounce through mechanism analysis;secondly, data augmentation is performed on the selected data;then, the Mahalanobis distance statistic of normal data is calculated and its distribution threshold is designed using the Kernel Density Estimation method;finally, the Mahalanobis distance is calculated for the augmented online data and compared to the threshold to determine if a bit bounce is present. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Anomaly detection

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