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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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Overview of Research on Transformation of Multi-AUV Formations

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complex System Modeling and Simulation 2021年第1期1卷 1-14页

作者： Bin Xin Junxi Zhang Jie Chen Qing Wang Yun Qu School of Automation and the State Key Laboratory of Intelligent Control and Decision of Complex SystemsBeijing Institute of TechnologyBeijing 100081China

Because of their wide detection range and rich functions,autonomous underwater vehicles(AUVs)are widely used for observing the marine environment,for exploring natural resources,for security and defense purposes,and in many other fields of *** with a single AUV,a multi-AUV formation can better perform various tasks and adapt to complex underwater *** changes in the mission or environment,a change in the UAV formation may also be *** the last decade,much progress has been made in the transformation of multi-AUV *** this paper,we aim to analyze the core concepts of multi-AUV formation transformation;summarize the effects of the AUV model,underwater environment,and communication between AUVs within formations on formation transformation;and elaborate on basic theories and implementation approaches for multi-AUV formation ***,this overview includes a bibliometric analysis of the related literature from multiple ***,some challenging issues and future research directions for multi-AUV formation transformation are highlighted.

关键词： autonomous underwater vehicle multi-AUV formation formation transformation formation control

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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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Pavement Cracks Coupled With Shadows:A New Shadow-Crack Dataset and A Shadow-Removal-Oriented Crack Detection Approach

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IEEE/CAA Journal of Automatica Sinica 2023年第7期10卷 1593-1607页

作者： Lili Fan Shen Li Ying Li Bai Li Dongpu Cao Fei-Yue Wang School of Information and Electronics Beijing Institute of TechnologyBeijing 100081China School of Civil Engineering Tsinghua UniversityBeijing 100084China School of Mechanical Engineering Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body and the College of Mechanical and Vehicle Engineering Hunan UniversityChangsha 410082China School of Vehicle and Mobility Tsinghua UniversityBeijing 100084China State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China

Automatic pavement crack detection is a critical task for maintaining the pavement stability and driving *** task is challenging because the shadows on the pavement may have similar intensity with the crack,which interfere with the crack detection *** to the present,there still lacks efficient algorithm models and training datasets to deal with the interference brought by the *** fill in the gap,we made several contributions as ***,we proposed a new pavement shadow and crack dataset,which contains a variety of shadow and pavement pixel size *** also covers all common cracks(linear cracks and network cracks),placing higher demands on crack detection ***,we designed a two-step shadow-removal-oriented crack detection approach:SROCD,which improves the performance of the algorithm by first removing the shadow and then detecting *** addition to shadows,the method can cope with other noise ***,we explored the mechanism of how shadows affect crack *** on this mechanism,we propose a data augmentation method based on the difference in brightness values,which can adapt to brightness changes caused by seasonal and weather ***,we introduced a residual feature augmentation algorithm to detect small cracks that can predict sudden disasters,and the algorithm improves the performance of the model *** compare our method with the state-of-the-art methods on existing pavement crack datasets and the shadow-crack dataset,and the experimental results demonstrate the superiority of our method.

关键词： Automatic pavement crack detection data augmentation compensation deep learning residual feature augmentation shadow removal shadow-crack dataset

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Field-Free Current-Induced Switching of L10-FePt Using Interlayer Exchange Coupling for Neuromorphic Computing

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Physical Review Applied 2023年第2期19卷 024034-024034页

作者： Kaifeng Dong Zhe Guo YiYi Jiao Ruofan Li Chao Sun Ying Tao Shuai Zhang Jeongmin Hong Long You School of Automation China University of Geosciences Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan 430074 China School of Optical and Electronic Information and Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan 430074 China

L10-phase FePt is well known for its unusually robust perpendicular magnetic anisotropy (PMA) properties arising from strong conduction-electron spin-orbit coupling (SOC) with the Fe orbital moment. The strong PMA enables stable magnetic storage and memory devices with ultrahigh capacity. Meanwhile, SOC is also the premise of the recently discovered spin-orbit-torque (SOT) effect, which opens avenues for possible electrical manipulation of magnetization for L10-FePt. The bulk SOT of the L10-FePt single layer was discovered recently; this leads to the magnetization of L10-FePt reversibly switching on itself. However, deterministic SOT switching of bulk perpendicularly magnetized FePt magnets relies on an external magnetic field to break the symmetry. Here, the symmetry-breaking issue is resolved by interlayer exchange coupling, where the FePt layer is coupled with an in-plane magnetized NiFe layer through a TiN spacer layer. Furthermore, our device also presents memristive or gradual switching behaviors, even without an external field, offering the potential for constructing spin synapses and spin neurons for neuromorphic computing. An artificial neural network with high accuracy (∼91.17%) is realized based on the constructed synapses and neurons. Our work paves the way for field-free SOT switching of single bulk PMA magnets and their potential applications in neuromorphic computing.

关键词： Exchange interaction Magnetic anisotropy Magnetization switching Spin-orbit torque Spintronics

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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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System Dynamics-Based Analysis on Factors Influencing Comprehensive Competence of Primary and Secondary School Students

System Dynamics-Based Analysis on Factors Influencing Compre...

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2022 Chinese automation Congress, CAC 2022

作者： Jing, Sifeng Liu, Xiwei Gong, Xiaoyan Zhao, Hongxia Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex Systems Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

With rapid progress of artificial intelligence (AI), it is urgent to promote educational reform to improve comprehensive competence level of primary and secondary school students (CCLPSSS). However, educational reform is a system engineering involving multiple and intertwined factors. In this research, a system dynamics-based model of education reform is developed. Then, based on this model, a series of simulation experiments are carried out to find main factors affecting improvement of CCLPSSS. Results show that: 1) this model could simulate trends of CCLPSSS under different educational reform measures;2) Measures including teaching mode reform, development and application of AI-based new teaching method, implementation of Double Reduction in compulsory education, implementation of deepening education evaluation reform policy, and development and application of AI-based education evaluation technology could play significant roles in promoting CCLPSSS and further strengthening cultivation of AI compound talents. © 2022 IEEE.

关键词： Students

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An Immersive Learning System with Multimodal Cognitive Processes Inference

An Immersive Learning System with Multimodal Cognitive Proce...

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2022 Chinese automation Congress, CAC 2022

作者： Bian, Siyu Liu, Xiwei Zhao, Hongxia Gong, Xiaoyan Jing, Sifeng The State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China Institute of Smart Education Systems Qingdao Academy of Intelligent Industries Qingdao266044 China

ISBN: (纸本)9781665465335

Using multimodal technology to infer learners' cognitive processes is an important means to support personalized learning. Previous cognitive process analysis is more about subjective reasoning, and less about combining physiological signals. With the rapid development of virtual reality technology, new requirements and more immersive schemes for learner's cognitive process inferring have been proposed. Based on this, this paper proposes an immersive learning system with multimodal cognitive processes inference, which is composed of macro, mesoscopic and micro environments. The system analyzes learner's actions, emotions and attention to infer learner's cognitive process through more comprehensive and objective multimodal data. The system also combines eye movement, brainwaves and other data, and proposes a multimodal data acquisition and analysis scheme using HMD-based VR. This paper also provides an immersive learning cognitive processes inference scheme for individual online learning, offline classroom learning scenarios. © 2022 IEEE.

关键词： Learning systems

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Research on intelligent methods in virtual reality games

Research on intelligent methods in virtual reality games

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2022 Chinese automation Congress, CAC 2022

作者： Wang, JiaWei Liu, Xiwei Gong, Xiaoyan Chinese Academy of Sciences The State Key Laboratory for Management Control of Complex Systems Institute of Automation Beijing100190 China Qingdao Academy of Intelligent Industries Institute of Smart Education Systems Qingdao266044 China

ISBN: (纸本)9781665465335

Introduction of virtual reality technology enriches the expression of traditional games and greatly improves the sense of substitution and interactive experience of users. Realization of virtual reality is inseparable from artificial intelligence technology. In order to improve immersion and interactivity of virtual reality games, it is necessary to propose more intelligent solutions to existing technologies. This paper aims to discuss some intelligent methods of virtual reality games. Firstly, it discusses artificial intelligence means to enhance the virtual reality experience, which can improve the tracking and force / tactile interaction system of existing virtual reality games from software and hardware aspects;Then it proposes the concept of human-machine hybrid enhanced intelligence: by introducing human cognitive model to improve perception and decision-making ability of virtual reality games, so as to make interaction of virtual reality games more friendly and more real. © 2022 IEEE.

关键词： Virtual reality

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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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