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Early detection of rotating stallin axial flow compressors via deterministic learning:detectability analysis

control Theory and Technology 2023年第2期21卷 161-172页

作者： Tianrui Chen Shuai Han Zejan Zhu Cong Wang School of Control Science and Engineering Shandong UniversityJinan 250061ShandongChina Center for Intelligent Medical Engineering Shandong UniversityJinan 250061ShandongChina School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641GuangdongChina

Rotating stall and surge are two violent unstable phenomena of an aero-engine *** early detection of rotating stall is a critical and difficult issue in the operation of a ***,a deterministic learning based stall inception detection approach(SIDA)has been developed for modeling and detecting stall inception in aero-engine *** paper considers the derivation of analytical results on the detection capabilities for the SIDA based on deterministic ***,by utilizing the input/output stability of the residual system,a detectability condition of the SIDA is presented,and how to choose the parameters of the diagnostic system is also ***,based on the relationship between NN approximation capabilities and radial basis function(RBF)network structures,the influence of RBF network structures on the performance properties of the SIDA is ***,a simulation study is presented,in which the Mansoux-C2 compressor model is utilized to verify the effectiveness of the proposed SIDA.

关键词： Axial compressor Rotating stall Surge Fault detection Deterministic learning Detectability condition

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Dynamic and Heterogeneous Identity-Based Cooperative Co-Evolution for Distributed Lot-Streaming Flowshop Scheduling Problem

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Complex System Modeling and Simulation 2025年第1期5卷 86-106页

作者： Wang, Juan Zhang, Guanghui Li, Xiaoling Feng, Yanxiang School of Information Science and Technology Hebei Agricultural University Baoding071001 China Xi'an Jiaotong University State Key Laboratory for Manufacturing Systems Engineering Xi'an710049 China School of Electronics and Control Engineering Chang'an University Xi'an710064 China School of Automation Science and Engineering Xi'an Jiaotong University Xi'an710049 China

In this research, a novel dynamic and heterogeneous identity based cooperative co-evolutionary algorithm (DHICCA) is proposed for addressing the distributed lot-streaming flowshop scheduling problem (DLSFSP) with the objective to minimize the makespan. A two-layer-vector representation is devised to bridge the solution space of DLSFSP and the search space of DHICCA. In the evolution of DHICCA, population individuals are endowed with heterogeneous identities according to their quality, including superior individuals, ordinary individuals, and inferior individuals, which serve local exploitation, global exploration, and diversified restart, respectively. Because individuals with different identities require different evolutionary mechanisms to fully unleash their respective potentials, identity-specific evolutionary operators are devised to evolve them in a cooperative co-evolutionary way. This is important to use limited population resources to solve complex optimization problems. Specifically, exploitation is carried out on superior individuals by devising three exploitative operators with different intensities based on techniques of variable neighborhood, destruction-construction, and gene targeting. Exploration is executed on ordinary individuals by a newly constructed discrete Jaya algorithm and a probability crossover strategy. In addition, restart is performed on inferior individuals to introduce new evolutionary individuals to the population. After the cooperative co-evolution, all individuals with different identities are merged as a population again, and their identities are dynamically adjusted by new evaluation. The influence of parameters on the algorithm is investigated based on design-of-experiment and comprehensive computational experiments are used to evaluate the performance of all algorithms. The results validate the effectiveness of special designs and show that DHICCA performs more efficient than the existing state-of-the-art algorithms in solving

关键词： Vector spaces

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A Non-Iterative Network Utilization Pricing Mechanism for P2P Energy Trading

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CSEE Journal of Power and Energy Systems 2024年第3期10卷 1301-1306页

作者： Yun Liu Yunqiang Gao Yuanzheng Li Jizhong Zhu Hoay Beng Gooi School of Electric Power Engineering South China University of TechnologyGuangzhou 510641China School of Artificial Intelligence and Automation Ministry of Education Key Laboratory of Image Processing and Intelligence ControlHuazhong University of Science and TechnologyWuhan 430074China School of Electrical and Electronic Engineering Nanyang Technological UniversitySingapore IEEE

To sustain operation and maintenance of an active distribution network(ADN),a network fee should be charged by the distribution network service provider(DNSP)for facilitating the P2P energy trading *** this end,this paper models the interaction among the DNSP and multiple prosumers as a Stackelberg game,and then develops a non-iterative and decentralized transactive mechanism to simultaneously achieve optimal network utilization pricing and peer-to-peer(P2P)*** results in an ADN with four prosumers connected to a common substation bus validate the effectiveness and efficiency of the proposed scheme.

关键词： Distribution network service provider network fee non-iterative peer-to-peer trading prosumer

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Brain-Conditional Multimodal Synthesis: A Survey and Taxonomy

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2025年第5期6卷 1080-1099页

作者： Mai, Weijian Zhang, Jian Fang, Pengfei Zhang, Zhijun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Southeast University School of Computer Science and Engineering Nanjing210096 China South China University of Technology Key Library of Autonomous Systems and Network Control Ministry of Education The School of Automation Science and Engineering Guangzhou510640 China Institute for Super Robotics Huangpu Guangzhou510555 China Nanchang University Jiangxi Thousand Talents Plan Nanchang330031 China Jishou University College of Computer Science and Engineering Jishou416000 China Guangdong Artificial Intelligence and Digital Economy Laboratory Pazhou Lab Guangzhou510335 China Shaanxi University of Technology Shaanxi Provincial Key Laboratory of Industrial Automation School of Mechanical Engineering Hanzhong723001 China Changsha Normal University School of Information Science and Engineering Changsha410100 China Guangdong University of Petrochemical Technology School of Automation Science and Engineering Institute of Artificial Intelligence and Automation Maoming525000 China

In the era of artificial intelligence generated content (AIGC), conditional multimodal synthesis technologies (e.g., text-to-image) are dynamically reshaping the natural content. Brain signals, serving as potential reflections of how the brain interprets external information, exhibit a distinctive one-to-many correspondence with various external modalities. This correspondence makes brain signals emerge as a promising guiding condition for multimodal synthesis (e.g., image, text, and audio), which is crucial for developing practical brain–computer interface systems and unraveling complex mechanisms underlying human perception. This survey comprehensively examines the emerging field of brain-conditional multimodal synthesis, termed AIGC-brain, to delineate the current landscape and future directions. To begin, related neuroimaging datasets and generative models are introduced as the foundation of AIGC-brain decoding and analysis. Next, we present a comprehensive taxonomy according to AIGC-brain methodologies, followed by task-specific representative work and implementation details to facilitate in-depth comparison and analysis. Quality assessments are then introduced for both qualitative and quantitative evaluation. Finally, this survey explores insights gained, outlining current challenges and prospects of AIGC-brain. As a pioneering survey, this article paves the way for future advances in AIGC-brain research. © 2020 IEEE.

关键词： Taxonomies

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Design method of end worm gear drive,including tooth curve geometry and improved lubrication angle formula

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Frontiers of Mechanical engineering 2024年第6期19卷 15-32页

作者： Jian CUI Yaping ZHAO Qingxiang MENG Zhiqiang HAO School of Mechanical Engineering and Automation Northeastern UniversityShenyang 110819China School of Mechanical Engineering Yanshan UniversityQinhuangdao 066004China Key Laboratory of Metallurgical Equipment and Control Technology of the Ministry of Education Wuhan University of Science and TechnologyWuhan 430081China

End worm gear drives are characterized by their multi-tooth contact,compact contour,and theoretical potential to overcome some inherent flaws of cylindrical worm ***,quantitative basic research on end worm gear drives is currently inadequate,which hinders the development of this *** work focuses on the computational design of end worm gear drives and proposes a new Niemann-type *** models of the proposed drive are established,and its engagement theory is deduced *** on the derived tooth surface equations,an innovative research methodology for the tooth curve geometry of the end worm gear is created,and the tooth curve in the worm gear reference plane is proved to be a *** improved formula for the lubrication angle is developed,which can provide more rational numerical results for the ***,the modified formula is universally applicable for line contact drives and can be used to quantitatively investigate the lubrication level between the teeth for the proposed *** outcomes demonstrate the favorable characteristics of the transmission,including broad conjugate areas,even contact lines,and fine global lubrication state.

关键词： end worm gear tooth curve geometry lubrication angle meshing characteristic nonlinear equation

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Predicting microbial extracellular electron transfer activity in paddy soils with soil physicochemical properties using machine learning

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science China(Technological sciences) 2024年第1期67卷 259-270页

作者： OU JiaJun LUO XiaoShan LIU JunYang HUANG LinYan ZHOU LiHua YUAN Yong School of Automation Guangdong University of TechnologyGuangzhou 510006China Guangdong Key Laboratory of Environmental Catalysis and Health Risk Control Guangzhou Key Laboratory Environmental Catalysis and Pollution ControlSchool of Environmental Science and EngineeringInstitute of Environmental Health and Pollution ControlGuangdong University of TechnologyGuangzhou 510006China School of Biomedical and Pharmaceutical Sciences Guangdong University of TechnologyGuangzhou 510006China

Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of ***,EET suffers from a lack of predictive ***,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output *** autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking *** 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble *** utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall *** study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.

关键词： extracellular electron transfer paddy soil machine learning prediction autoencoder ensemble stacking model

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2D Human Pose Estimation with Residual Upsampling and Spatial Feature Enhancement 43

2D Human Pose Estimation with Residual Upsampling and Spatia...

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43rd Chinese control Conference, CCC 2024

作者： Li, Shanbin Zheng, Xishan South China University of Technology School of Automation Science and Engineering Guangzhou China Changji University School of Energy and Control Engineering Xinjiang China

ISBN: (纸本)9789887581581

Computer vision, which has been extensively used in intelligent monitoring, self-driving, medical assistance, sports analysis, and other fields, includes human pose estimation as a key component. Human pose estimation algorithms have been appearing nonstop in recent years, and as they get more complex and structured, it becomes harder to analyze and actually implement the algorithms. SBL network is surprisingly efficient for such a simple design. This study makes several modifications based on SBL to further enhance its performance. In order to fuse rich features from the feature extraction stage to the upsampling phase, we first introduce the residual upsampling module (RUSM). This guarantees that more detailed, high-resolution information can be utilized to upsampling features, significantly increasing the accuracy of joint recognition in the human body. Additionally, we present the spatial feature enhancement module (SFEM), which utilizes the spatial attention mechanism with the residual structure. It is employed to boost spatial features of the upsampling phase and further increase the detection precision of the network. Lastly, we conduct numerous experiments on the LSP dataset, which amply illustrates the enhanced functionality of the extra modules and the better performance of our suggested network. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Image enhancement

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Coronary Artery Segmentation in X-ray Angiography Based on Deep Learning Approach 43

Coronary Artery Segmentation in X-ray Angiography Based on D...

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43rd Chinese control Conference, CCC 2024

作者： Li, Shanbin Fan, Yiting South China University of Technology School of Automation Science and Engineering Guangzhou China Changji University School of Energy and Control Engineering Xinjiang China

ISBN: (纸本)9789887581581

Segmenting coronary arteries from X-ray coronary angiography (XCA) images allows observation of coronary artery morphology and stenosis, which is of great significance for computer-aided diagnosis and coronary artery disease treatment. However, XCA images have low contrast and irregular lighting due to the limits of existing imaging techniques, making vascular segmentation challenging. Segmentation of coronary arteries using conventional U-shaped segmentation networks is challenging due to the problem of major differences in vascular morphology, size, and overlap. This paper proposes a network based on the U-shape structure for multiscale context information fusion with lightweight GhostNetV2 as the backbone feature extraction network to improve the model's feature extraction capabilities. Then a multi-scale context fusion module (MCF) is proposed to effectively capture the contextual information of blood vessels. Finally, we propose a feature re-extraction module (FRM) to achieve effective fine feature re-extraction in complex backgrounds. Experimental results show that the model we propose achieves more accurate coronary artery segmentation in complex backgrounds with fewer parameters, improves the segmentation of fine vessels at the end of coronary arteries, and performs well compared with other artery segmentation models. The F1 score, Intersection over Union (IOU), accuracy (ACC), and Sensitivity (Sen) of the model can reach 82.23 %, 69.83 %, 98.79 %, 81.93 %, respectively. © 2024 Technical Committee on control Theory, Chinese Association of automation.

关键词： Deep learning

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Deep Co-Training Partial Least Squares Model for Semi-Supervised Industrial Soft Sensing

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3363-3371页

作者： Zheng, Junhua Zhou, Le Ye, Lingjian Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou310023 China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Data-driven soft sensing has become quite popular in recent years, which can provide real-time estimations of key variables in industrial processes. While the introduction of deep learning does improve the prediction performance, it is highly restricted to the number of labeled training data, as well as large computational burden and cumbersome parameter tuning procedures. How to break through the bottleneck of data-drive models in terms of limited labeled data and high computational complexity should be one of the main recent focuses in the field of industrial soft sensing. In this article, a deep co-training PLS (deep CT-PLS) model is proposed to extend the ordinary PLS model to the semi-supervised deep form. While the deep model can efficiently extract inherent natures of process data, the co-training strategy makes lots of unlabeled data useful through a two-view cross training and annotation process. In this case, the performance restriction of the deep PLS model can be greatly relieved, with the incorporation of additional unlabeled data, while at the same time the designed model structure keeps in a low computational complexity. Based on the case study on a real industrial production process, the deep CT-PLS model can significantly improve the soft sensing performance. © 2013 IEEE.

关键词： Self-supervised learning

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Prescribed-Time Nash Equilibrium Seeking for Pursuit-Evasion Game

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IEEE/CAA Journal of Automatica Sinica 2024年第6期11卷 1518-1520页

作者： Lei Xue Jianfeng Ye Yongbao Wu Jian Liu D.C.Wunsch Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationNanjing 210096 School of Automation Southeast UniversityNanjing 210096China Department of Electrical and Computer Engineering Missouri University of Science and TechnologyRollaMO 65409 USA IEEE

Dear Editor,This letter is concerned with prescribed-time Nash equilibrium(PTNE)seeking problem in a pursuit-evasion game(PEG)involving agents with second-order *** order to achieve the prior-given and user-defined convergence time for the PEG,a PTNE seeking algorithm has been developed to facilitate collaboration among multiple pursuers for capturing the evader without the need for any global ***,it is theoretically proved that the prescribedtime convergence of the designed algorithm for achieving Nash equilibrium of ***,the effectiveness of the PTNE method was validated by numerical simulation results.A PEG consists of two groups of agents:evaders and *** pursuers aim to capture the evaders through cooperative efforts,while the evaders strive to evade *** is a classic noncooperative *** has attracted plenty of attention due to its wide application scenarios,such as smart grids[1],formation control[2],[3],and spacecraft rendezvous[4].It is noteworthy that most previous research on seeking the Nash equilibrium of the game,where no agent has an incentive to change its actions,has focused on asymptotic and exponential convergence[5]-[7].

关键词： seeking prescribed convergence

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