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Identifying dynamical characteristics of directed functional epilepsy networks from eigenmodes

science China Technological sciences 2025年第5期68卷 203-214页

作者： Songan HOU Haodong WANG Ying YU Xiaotong LIU Qingyun WANG Department of Dynamics and Control Beihang University Ningxia Basic Science Research Center of Mathematics Ningxia University

Epilepsy often accompanies cognitive impairments, which are featured by dynamics of EEG data. The eigenmode method, combined with functional networks derived from EEG data, provides a valid method to investigate dynamical characteristics of the brain's integration and segregation while establishing connections with cognitive function. Based on the transfer entropy method, we utilize the eigenmode approach to analyze SEEG data from epilepsy patients, which extends the theory of eigenmode hierarchical modules to directed functional networks. This work mainly refines and employs the dynamical characteristics from the eigenmodes of the epilepsy directional functional networks, including integration and segregation theories and proposes the network's functional recombination rate feature. Results indicate that directed functional networks constructed through transfer entropy can also manifest the phenomenon of hierarchical modules in brain functional modes. In addition, during epileptic seizures, higher layers of overall integration features and increased functional recombination rates are observed. Furthermore, alterations in the aggregation of prominent nodes within the eigenmodes of epilepsy patients are noted during seizure episodes. This paper provides an improved method for the analysis of dynamical features of directed epilepsy network, which may potentially provide help and new understanding for the analysis of functional features of epilepsy.

关键词： epilepsy eigemode method integration and segregation functional recombination dynamical features

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Unsupervised Range-Nullspace Learning Prior for Multispectral Images Reconstruction

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IEEE Transactions on Image Processing 2025年 34卷 2513-2528页

作者： Chen, Yurong Wang, Yaonan Zhang, Hui Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology College of Electrical and Information Engineering Hunan Changsha410082 China Hunan University National Engineering Research Center of Robot Visual Perception and Control Technology School of Robotics Hunan Changsha410082 China

Snapshot Spectral Imaging (SSI) techniques, with the ability to capture both spectral and spatial information in a single exposure, have been found useful in a wide range of applications. SSI systems generally operate within the ‘encoding-decoding’ framework, leveraging the synergism of optical hardware and reconstruction algorithms. Typically, reconstructing desired spectral images from SSI measurements is an ill-posed and challenging problem. Existing studies utilize either model-based or deep learning-based methods, but both have their drawbacks. Model-based algorithms suffer from high computational costs, while supervised learning-based methods rely on large paired training data. In this paper, we propose a novel Unsupervised range-Nullspace learning (UnNull) prior for spectral image reconstruction. UnNull explicitly models the data via subspace decomposition, offering enhanced interpretability and generalization ability. Specifically, UnNull considers that the spectral images can be decomposed into the range and null subspaces. The features projected onto the range subspace are mainly low-frequency information, while features in the nullspace represent high-frequency information. Comprehensive multispectral demosaicing and reconstruction experiments demonstrate the superior performance of our proposed algorithm. © 1992-2012 IEEE.

关键词： Image reconstruction

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Efficient feature selection for enhanced chiller fault diagnosis:A multi-source ranking information-driven ensemble approach

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Building Simulation 2025年第1期18卷 141-159页

作者： Zhanwei Wang Penghua Xia Jingjing Guo Sai Zhou Lin Wang Yu Wang Chunxiao Zhang Institute of Building energy and Thermal Science Henan University of Science and TechnologyLuoyang471023China Henan Provincial Engineering Research Center of Building Environmental Control and Safety Luoyang471023China

Fault diagnosis(FD)is essential for ensuring the reliable operation of chillers and preventing energy *** selection(FS)is a critical prerequisite for effective ***,current FS methods have two major ***,most approaches rely on single-source ranking information(SSRI)to evaluate features individually,which results in non-robust outcomes across different models and datasets due to the one-sided nature of ***,thermodynamic mechanism features are often overlooked,leading to incomplete initial feature libraries,making it challenging to select optimal features and achieve better diagnostic *** address these issues,a robust ensemble FS method based on multi-source ranking information(MSRI)is *** employing an efficient strategy based on maximizing relevance while proper redundancy,the MSRI method fully leverages Mutual Information,Information Gain,Gain Ratio,Gini index,Chi-squared,and Relief-F from both qualitative and quantitative ***,comprehensive consideration of thermodynamic mechanism features ensures a complete initial feature *** a methodological standpoint,a general framework for constructing the MSRI-based FS method is *** proposed method is applied to chiller FD and tested across ten widely-used machine learning *** optimized features are selected from the original set of forty-two,achieving an average diagnostic accuracy of 98.40%and an average F-measure above 94.94%,demonstrating the effectiveness and generalizability of the MSRI *** to the SSRI approach,the MSRI method shows superior robustness,with the standard deviation of diagnostic accuracy reduced by 0.03 to 0.07 and an improvement in diagnostic accuracy ranging from 2.53%to 6.12%.Moreover,the MSRI method reduced computation time by 98.62%compared to wrapper methods,without sacrificing accuracy.

关键词： chiller feature selection fault diagnosis multi-source ranking information machine learning

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Deep Neural Network With Local–Global Context Aggregation and Self-Distillation for Fish Counting in Deep-Sea Aquaculture

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IEEE Sensors Journal 2025年第9期25卷 16411-16424页

作者： Liu, Hanchi Ma, Xin Li, Haoran Shandong University Center for Robotics School of Control Science and Engineering Jinan250061 China

Vision-based fish counting plays a vital role in monitoring breeding density, optimizing feeding strategies, and planning marketing schedules in deep-sea aquaculture. However, large-scale variations in fish and nonuniform background illumination in underwater images make it challenging to accurately count fish in deep-sea cages. Aiming to solve these issues, this study proposes a deep neural network (DNN) with local-global context aggregation and self-distillation called LGSDNet for fish counting and density estimation in deep-sea aquaculture. First, a local-global context aggregation module (LGCAM) is designed to aggregate the dense local multiscale context and global context in images, enabling the network to capture robust feature representations for fish with various scales under various background illumination conditions. Then, a self-distillation module (SDM) is designed to leverage information from the deep layers of the network to guide the learning of the shallow layers, enhancing the representation learning of the network without increasing the inference time. Extensive comparative experiments on the fish counting dataset collected from a deep-sea cage demonstrate the effectiveness of LGSDNet. It achieves a mean absolute error (MAE) of 5.68, a root-mean-squared error (RMSE) of 7.38, and a mean absolute percentage error (MAPE) of 3.27%, outperforming the Baseline with a reduction in the aforementioned metrics by 6.75, 7.92, and 3.42%, respectively. In addition, LGSDNet outperforms state-of-the-art fish and crowd counting methods on the dataset while having only 13.03 M parameters. Generalization experiments further demonstrate the adaptability of LGSDNet to diverse aquaculture environments. © 2001-2012 IEEE.

关键词： Fish

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Adaptive control of a Flexible Manipulator With Unknown Hysteresis and Intermittent Actuator Faults

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 148-158页

作者： Shouyan Chen Weitian He Zhijia Zhao Yun Feng Zhijie Liu Keum-Shik Hong School of Mechanical and Electrical Engineering Guangzhou University the School of Control Science and Engineering Shandong University IEEE the College of Electrical and Information Engineering Hunan University the National Engineering Research Center for Robot Visual Perception and Control Technology Hunan University the School of Intelligence Science and Technology University of Science and Technology Beijing (USTB) the Institute for Future the School of AutomationQingdao University the School of Mechanical Engineering Pusan National University

In this study, we consider a single-link flexible manipulator in the presence of an unknown Bouc-Wen type of hysteresis and intermittent actuator faults. First, an inverse hysteresis dynamics model is introduced, and then the control input is divided into an expected input and an error compensator. Second,a novel adaptive neural network-based control scheme is proposed to cancel the unknown input hysteresis. Subsequently,by modifying the adaptive laws and local control laws, a fault-tolerant control strategy is applied to address uncertain intermittent actuator faults in a flexible manipulator system. Through the direct Lyapunov theory, the proposed scheme allows the state errors to asymptotically converge to a specified interval. Finally,the effectiveness of the proposed scheme is verified through numerical simulations and experiments.

关键词： Adaptive control flexible manipulator intermittent actuator faults inverse hysteresis dynamics vibration control

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Exploration of the interaction mechanism in the synergistic degradation of benzene and toluene over MnCoO_(x) catalysts

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Frontiers of Environmental science & engineering 2025年第2期19卷 119-134页

作者： Xin Xing Zhe Li Yixin Wang Zonghao Tian Jie Cheng Zhengping Hao College of Environment and Resources Taiyuan University of Science and TechnologyTaiyuan 030024China National Engineering Laboratory for VOCs Pollution Control Material&Technology Research Center for Environmental Material and Pollution Control TechnologyUniversity of Chinese Academy of SciencesBeijing 101408China

The catalytic degradation of single-component VOCs has been widely ***,several types of VOCs may be present in an actual industrial emission *** synergistic removal of multicomponent VOCs is currently a popular research ***,Mn–Co samples with various Mn/Co ratios(1:2,1:1,and 2:1)were successfully prepared,and the catalytic oxidation performance characteristics toward benzene and toluene over these samples under single and binary VOC oxidation conditions were *** with pure MnO_(x) and CoO_(x),the prepared Co–Mn composite oxide samples exhibited significantly improved catalytic ***_(x) and MnCo_(2)O_(x) showed optimum catalytic performance,with 100%benzene and 100%toluene conversion in the mixtures at 300 and 350°C,100%CO_(2) *** methods were employed to elucidate the relevance of the catalytic activity to the structures,acidity,redox properties,Mn–Co valence state and oxygen ***,the interactions between benzene and toluene during their synergistic degradation,as well as the intermediates and potential reaction mechanisms of their simultaneous elimination,were *** Mn–Co oxide compounds for cooperative catalytic oxidation of benzene and toluene represents a viable and effective technique for the practical and synergist elimination of multicomponent VOCs.

关键词： Mn-Co oxides Synergetic catalytic oxidation Benzene and toluene Reaction mechanism

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Release characteristics of arsenic from sediments and its source or sink competition with phosphorus:A case of a great lake with grass-algae alternation

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Journal of Environmental sciences 2025年第3期149卷 278-287页

作者： Shuhang Wang Yongsheng Chang Wei Huang Dianhai Yang Feifei Che State Key Laboratory of Pollution Control and Resource Reuse College of Environmental Science and EngineeringTongji UniversityShanghai 200092China College of Environmental Science and Engineering Donghua UniversityShanghai 201620China National Engineering Laboratory for Lake Pollution Control and Ecological Restoration Chinese Research Academy of Environmental SciencesBeijing 100012China

The arsenic(As)release from sediments in great lakes is affected by various *** this study,the characteristics of As release fromsediments was investigated,and the As sources and sinks with the strengths in sediments from different areas(grass-type,algae-type,and grass-algae alternation areas)in great shallow lakes(Taihu Lake,China)were analyzed,and the influence of P competition in the process of As release was also *** results showed that changing trend of the values of equilibrium As concentration in sediments were consistent with the regional changes(0 to 28.12μg/L),and the sediments from algaetype areas had the higher *** sediments from western lake and northwest lake bay were a strong As and a weak P source,and the north lake bay had the opposite trend of these two *** P source competition with As from the sediments occurred in algae-type *** grass-type areas had strong As and P retention capacities,indicating a sink role of sediment with high As and P sorption *** degree of As and P saturation had similar trend in sediments,and the grass-type areas had the higher values,18.3%-21.4%and 15.31%-20.34%,*** analysis results showed that most of As release contribution was from the bottom(30-50 cm)sediments,and the surface(0-10 cm)sediments from algae-type areas contributed more to the overlying water than other region.

关键词： Arsenic Phosphorus Sediment Source and sink Contribution

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Surfactant-mediated transport of copper oxide nanoparticles in porous media: Effects of electrolytes, phosphate and organic matter

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Chemical engineering research and Design 2025年 217卷 283-294页

作者： Wang, Yujue Wu, Ming Hao, Yanru Li, Hui Mo, Cehui Guangdong Provincial Research Center for Environment Pollution Control and Remediation Materials Engineering Research Center of Tropical and Subtropical Aquatic Ecological Engineering Ministry of Education College of Life Science and Technology Jinan University Guangzhou510632 China

This study investigates the transport behavior of copper oxide nanoparticles (nCuO) in saturated porous media mediated by the cationic surfactant cetyltrimethylammonium bromide (CTAB) and the anionic surfactant sodium dodecyl sulfate (SDS). The effects of ion types and concentrations, humic acid (HA), and phosphate on nCuO mobility were systematically examined. Under CTAB conditions, low ion concentrations enhanced nCuO transport by reducing interfacial tension (IFT), with calcium ions (Ca2 +) exhibiting a stronger promotive effect than sodium ions (Na+). However, higher ion concentrations subsequently inhibited nCuO transport. The addition of HA and phosphate promoted nCuO deposition by diminishing the electrostatic repulsion between nCuO and quartz sand. In SDS-mediated systems, increasing ion concentrations suppressed nCuO transport and exhibited "ripening" phenomena. The introduction of HA in SDS systems initially inhibited and then enhanced nCuO transport, with breakthrough curves under inhibitory conditions also showing "ripening". Notably, phosphate enhanced nCuO transport by increasing the electronegativity of quartz sand, with the promotive effect being more pronounced at low phosphate concentrations compared to high concentrations. Overall, the type of surfactant significantly influences the mobility of nCuO under various environmental conditions. These findings advance the understanding of nanoparticle behavior in environmental systems and provide valuable insights for the development of strategies in environmental protection and pollution remediation. © 2025 Institution of Chemical Engineers

关键词： Quartz

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Dynamic Modeling and control for an Offshore Semisubmersible Floating Wind Turbine

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IEEE Transactions on Automation science and engineering 2025年 22卷 12371-12382页

作者： Gong, Yingjie Yang, Qinmin Geng, Hua Meng, Wenchao Wang, Lin Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Huzhou Institute Zhejiang University State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Hangzhou310027 China Tsinghua University Beijing National Research Center for Information Science and Technology Department of Automation Beijing100084 China Zhejiang Windey Company Ltd. Hangzhou310027 China

Floating wind turbines (FWTs) hold significant potential for the exploitation of offshore renewable energy resources. Nevertheless, prior to the construction of FWTs, it is imperative to tackle several critical challenges, especially the issue of performance degradation under combined wind and wave loads. This study initiates with the development of a simplified nonlinear dynamical model for a semi-submersible FWT. In particular, both the rotor dynamics and the finite rotations of the platform are considered in presented modeling approach, thereby effectively capturing the complex interplay between the platform, tower, nacelle, and rotor under combined wind and wave loads. Subsequently, based on the developed FWT model, a novel adaptive nonlinear pitch controller is formulated with the goal of striking a trade-off between regulating power generation and reducing platform motion. Notably, the proposed control strategy adopts a continuous control approach, strategically beneficial in circumventing the chattering phenomenon commonly associated with sliding mode control. Furthermore, the controller integrates an online approximator and a robust integral of the sign of the tracking error, facilitating real-time learning of system unknown dynamics while compensating for bounded disturbances. Finally, both the accuracy of the established nonlinear FWT model in predicting key dynamics and the superiority of the presented pitch controller are validated through comprehensive comparative studies. Note to Practitioners - This paper addresses the conflicting goals between power regulation and load mitigation for floating wind turbines (FWTs) to ensure the reliable operation of wind turbine systems. This remains an ongoing challenge due to the inherent complexity of existing FWT models, frequently resulting in controllers crafted using linearized representations that fail to accommodate real-world uncertainties effectively. Through the utilization of a simplified physical-based n

关键词： Renewable energy

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Fault Diagnosis for Railway Point Machines Using VMD Multi-Scale Permutation Entropy and ReliefF Based on Vibration Signals

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Chinese Journal of Electronics 2025年第1期34卷 204-211页

作者： Yongkui Sun Yuan Cao Peng Li Shuai Su National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University School of Automation and Intelligence Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University

The railway point machine plays an important part in railway systems. It is closely related to the safe operation of trains. Considering the advantages of vibration signals on anti-interference, this paper develops a novel vibration signal-based diagnosis approach for railway point machines. First, variational mode decomposition(VMD)is adopted for data preprocessing, which is verified more effective than empirical mode decomposition. Next, multiscale permutation entropy is extracted to characterize the fault features from multiple scales. Then ReliefF is utilized for feature selection, which can greatly decrease the feature dimension and improve the diagnosis accuracy. By experiment comparisons, the proposed approach performs best on diagnosis for railway point machines. The diagnosis accuracies on reverse-normal and normal-reverse processes are respectively 100% and 98.29%.

关键词： Vibrations Fault diagnosis Accuracy Data preprocessing Switches Feature extraction Rail transportation Entropy Maintenance Monitoring

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