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第41届中国控制会议

作者： Wangnian Li Min Wu Ke Yao Quanxin Li Chengda Lu Sheng Du School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Directional drilling trajectory control technology is one of the key technologies to realize automation and intelligence of underground tunnel drilling in coal *** construction of directional drilling in underground coal mines mainly relies on manual observation of measurement-while-drilling parameters to control the drilling trajectory,which is highly dependent on manual *** paper designs an intelligent control system of underground directional drilling trajectory for coal *** the idea of hierarchical design and modular design,this system collects and displays geometric parameters,geological parameters,and engineering parameters of the directional drilling ***,an intelligent decision-making platform for directional drilling trajectory prediction and control is developed,laying a foundation for the research of directional drilling trajectory prediction and intelligent control for coal mines.

关键词： Trajectory prediction trajectory control geometric parameters geological parameters engineering parameters

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Switched flow matching: eliminating singularities via switching ODEs 24

Switched flow matching: eliminating singularities via switch...

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Proceedings of the 41st International Conference on Machine Learning

作者： Qunxi Zhu Wei Lin Research Institute of Intelligent Complex Systems Fudan University China Research Institute of Intelligent Complex Systems and School of Mathematical Sciences LMNS and SCMS and State Key Laboratory of Medical Neurobiology and MOE Frontiers Center for Brain Science Institutes of Brain Science Fudan University China and Shanghai Artificial Intelligence Laboratory China

Continuous-time generative models, such as Flow Matching (FM), construct probability paths to transport between one distribution and another through the simulation-free learning of the neural ordinary differential equations (ODEs). During inference, however, the learned model often requires multiple neural network evaluations to accurately integrate the flow, resulting in a slow sampling speed. We attribute the reason to the inherent (joint) heterogeneity of source and/or target distributions, namely the singularity problem, which poses challenges for training the neural ODEs effectively. To address this issue, we propose a more general framework, termed Switched FM (SFM), that eliminates singularities via switching ODEs, as opposed to using a uniform ODE in FM. Importantly, we theoretically show that FM cannot transport between two simple distributions due to the existence and uniqueness of initial value problems of ODEs, while these limitations can be well tackled by SFM. From an orthogonal perspective, our framework can seamlessly integrate with the existing advanced techniques, such as minibatch optimal transport, to further enhance the straightness of the flow, yielding a more efficient sampling process with reduced costs. We demonstrate the effectiveness of the newly proposed SFM through several numerical examples.

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Automation 5.0: The key to systems Intelligence and Industry 5.0

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IEEE/CAA Journal of Automatica Sinica 2024年第8期11卷 1723-1727页

作者： Ljubo Vlacic Hailong Huang Mariagrazia Dotoli Yutong Wang Petros A.Ioannou Lili Fan Xingxia Wang Raffaele Carli Chen Lv Lingxi Li Xiaoxiang Na Qing-Long Han Fei-Yue Wang Institute of Intelligent and Integrated Systems and the School of Engineering and Built Environment Griffith UniversityNathanQLD 4111Australia Department of Aeronautical and Aviation Engineering The Hong Kong Polytechnic UniversityHong KongChina Department of Electrical and Information Engineering Polytechnic of Bari70126 BariItaly State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190and also with the Qingdao Academy of Intelligent IndustriesQingdao 266114China Department of Electrical Engineering-Systems University of Southern CaliforniaLos AngelesCA 90007 USA School of Automation Beijing Institute of TechnologyBeijing 100081China State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049 Beijing Huairou Academy of Parallel Sensing Beijing 101499China School of Mechanical and Aerospace Engineering Nanyang Technological UniversitySingapore 639798Singapore Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University IndianapolisIndianapolisIN 46202 USA Department of Engineering University of CambridgeCB21TN CambridgeU.K. School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyMelbourne VIC 3122Australia State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China Dazhou Artificial Intelligence Institute Dazhouand the Faculty of Innovation EngineeringMacao University of Science and TechnologyMacao 999078China

AUTOMATION has come a long way since the early days of mechanization,i.e.,the process of working exclusively by hand or using animals to work with *** rise of steam engines and water wheels represented the first generation of industry,which is now called Industry Citation:***,***,***,***,***,***,***,***,***,***,***,Q.-***,and F.-***,“Automation 5.0:The key to systems intelligence and Industry 5.0,”IEEE/CAA ***,vol.11,no.8,pp.1723-1727,Aug.2024.

关键词： Automation machinery intelligence

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RUIFAF-Net: Radiofrequency and Ultrasound Image Feature Attention and Fusion Network for Improved Breast Cancer Segmentation

RUIFAF-Net: Radiofrequency and Ultrasound Image Feature Atte...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Wu, Zhenxu Li, Bo Zhou, Huajun Lang, Xun He, Bingbing Li, X Allen Zhang, Yufeng Lv, Wenbing Guangdong Hybribio Biotechnology Co. Ltd Guangdong Chaozhou521000 China Yunnan University School of Information Yunnan Kunming650504 China Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan Kunming650504 China Dianchi College Yunnan University Yunnan Kunming650228 China

ISBN: (纸本)9798350386226

Ultrasound imaging (US) is one of the most commonly used techniques for detecting breast lesions. However, due to the inherent properties of low contrast, speckle noise, and blurred boundaries in B-mode ultrasound images, the performance of breast lesion segmentation was significantly limited. In contrast, original radiofrequency (RF) data contains more detailed information. To enhance the performance of breast lesion segmentation and compensate for the information missing in ultrasound images, this study proposed a radiofrequency and ultrasound image feature attention and fusion network (RUIFAF Net) to comprehensively fuse the complementary information from both modalities. Specifically, we proposed a Feature Attention Module (FAM) and a Multi-level Fusion (MF) method to obtain multi-scale feature information and integrate contextual semantic information, respectively. Experimental results on OASBUD dataset demonstrate that our proposed RUIFAF-Net achieved higher Dice (0.8264 vs. 0.6468-0.8184) and IoU (0.7161 vs. 0.5243-0.7025) compared with the other six advanced methods. © 2024 IEEE.

关键词： Ultrasonic imaging

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An Improved Variational Mode Decomposition with Adaptive Initial Center Frequency for Bearing Fault Feature Extraction

An Improved Variational Mode Decomposition with Adaptive Ini...

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Chinese Automation Congress (CAC)

作者： Ying Wang Xun Lang Jiande Wu Yuzhen Li Peng Li Yufeng Zhang Department of Electronic Engineering Information School Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

Variational mode decomposition (VMD) is extensively utilized in the field of industrial signal processing due to its superior anti-mixing and denoising capabilities. However, in practice, the performance of VMD is significantly influenced by the initial center frequency $\omega^{1}$ . An improper setting of $\omega^{1}$ can significantly reduce the stability of decomposition, resulting in the loss or masking of feature information by noise. To address the above issue, an improved VMD algorithm is proposed in this work. Our algorithm redefines $\omega^{1}$ based on the narrowband characteristics of intrinsic mode function (IMF). Under the new definition, $\omega^{1}$ can be adaptively adjusted based on the signal spectrum processed by Wiener filtering. This adjustment significantly enhances the flexibility and anti-mixing performance of the original VMD. To validate our proposed method, comparative experiments were conducted using two sets of fault data from Case Western Reserve University (CWRU). The results demonstrate that our method is more effective and superior in extracting fault features.

关键词： Vibrations Fault diagnosis Wiener filters Automation Noise Signal processing algorithms Feature extraction Maintenance Optimization Narrowband

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Estimation and compensation of periodic disturbance using internal-model-based equivalent-input-disturbance approach

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Science China(Information Sciences) 2022年第8期65卷 150-163页

作者： Qicheng MEI Jinhua SHE Zhentao LIU Min WU School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Engineering Tokyo University of Technology

This paper presents an improved equivalent-input-disturbance(EID) approach to deal with periodic disturbances. The approach has two degrees of freedom. One is an improved EID compensator, in which a repetitive controller is inserted in this study. The other is a conventional servo system for a reference *** improved EID compensator estimates and compensates for periodic disturbances without steady-state error, and the servo system ensures a satisfactory tracking performance. The improved EID compensator is designed using the linear-matrix-inequality(LMI) method. Three parameters in an LMI are selected using the particle-swarm-optimization(PSO) algorithm. The state-feedback gain of the conventional servo system is designed using the linear-quadratic-regulator(LQR) method. Simulation results of a rotational control system demonstrate the validity of the approach and its advantage over others.

关键词： equivalent input disturbance (EID) linear matrix inequality (LMI) linear quadratic regulator (LQR) particle swarm optimization (PSO) periodic disturbance repetitive control

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Progress of the Impact of Terahertz Radiation on Ion Channel Kinetics in Neuronal Cells

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Neuroscience Bulletin 2024年第12期40卷 1960-1974页

作者： Yanjiang Liu Xi Liu Yousheng Shu Yuguo Yu Research Institute of Intelligent and Complex Systems Fudan UniversityShanghai200433China MOE Frontiers Center for Brain Science and State Key Laboratory of Medical Neurobiology Fudan UniversityShanghai200433China Institute of Science and Technology for Brain-Inspired Intelligence Fudan UniversityShanghai200433China Department of Neurology Huashan HospitalFudan UniversityShanghai20043China Institute for Translational Brain Research Fudan UniversityShanghai200433China Department of Neurosurgery Jinshan Hospital of Fudan UniversityShanghai201508China Shanghai Artificial Intelligence Laboratory Shanghai200232China

In neurons and myocytes,selective ion channels in the plasma membrane play a pivotal role in transducing chemical or sensory stimuli into electrical signals,underpinning neural and cardiac *** advancements in biomedical research have increasingly spotlighted the interaction between ion channels and electromagnetic fields,especially terahertz(THz)*** review synthesizes current findings on the impact of THz radiation,known for its deep penetration and non-ionizing properties,on ion channel kinetics and membrane fluid *** is organized into three parts:the biophysical effects of THz exposure on cells,the specific modulation of ion channels by THz radiation,and the potential pathophysiological consequences of THz *** the biophysical mechanisms underlying these effects could lead to new therapeutic strategies for diseases.

关键词： Ion channel kinetics Terahertz radiation Electromagnetic field interactions Neuron Cell membrane

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Design of an intelligent Monitoring System for Operating States in a Coal Mine Drilling Process 41

Design of an Intelligent Monitoring System for Operating Sta...

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第41届中国控制会议

作者： Xiao Yang Luefeng Chen Sheng Du Chengda Lu Wangnian Li Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Xi'an Research Institute Co.Ltd. China Coal Technology and Engineering Group

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development trend of intelligent drilling in coal mines,an intelligent monitoring system for operating states in a drilling process is developed to solve the problems of incomplete display of drilling parameters and ineffective *** system realizes the remote monitoring of drilling *** mainly adopts the Fuzzy C-Means clustering algorithm to cluster the drilling *** it combines expert empirical knowledge to classify coal seams into soft,hard and alternating soft and hard coal *** boundary values are calculated according to the boundary ***,three drilling operating states,rated,conservative and over-excited,are judged to provide guidance for the drilling process control.

关键词： Drilling process intelligent monitoring Fuzzy C-Means clustering Operating states

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TIME-INDEPENDENT SPIKING NEURON VIA MEMBRANE POTENTIAL ESTIMATION FOR EFFICIENT SPIKING NEURAL NETWORKS

arXiv

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arXiv 2024年

作者： Chen, Hanqi Yu, Lixing Zhan, Shaojie Yao, Penghui Shao, Jiankun School of Information Science and Engineering Yunnan University Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan China State Key Laboratory of Explosion Science and Technology Beijing Institute of Technology Beijing China

The computational inefficiency of spiking neural networks (SNNs) is primarily due to the sequential updates of membrane potential, which becomes more pronounced during extended encoding periods compared to artificial neural networks (ANNs). This highlights the need to parallelize SNN computations effectively to leverage available hardware parallelism. To address this, we propose Membrane Potential Estimation Parallel Spiking Neurons (MPE-PSN), a parallel computation method for spiking neurons that enhances computational efficiency by enabling parallel processing while preserving the intrinsic dynamic characteristics of SNNs. Our approach exhibits promise for enhancing computational efficiency, particularly under conditions of elevated neuron density. Empirical experiments demonstrate that our method achieves state-of-the-art (SOTA) accuracy and efficiency on neuromorphic datasets. Codes are available at https://***/chrazqee/MPE-PSN. © 2024, CC BY.

关键词： Neurons

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Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments 13

Exchange Strategies for Multi-Colony Ant Algorithms in Dynam...

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13th IEEE Congress on Evolutionary Computation, CEC 2024

作者： Mavrovouniotis, Michalis Li, Changhe Yazdani, Danial Handjimitsis, Diofantos Eratosthenes Centre of Excellence Limassol Cyprus Cyprus University of Technology Department of Civil Engineering and Geomatics Limassol Cyprus School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China University of Technology Sydney Faculty of Engineering & Information Technology Sydney Australia

ISBN: (纸本)9798350308365

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum. © 2024 IEEE.

关键词： Ant colony optimization

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