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13th IEEE International Conference on CYBER Technology in Automation, control, and Intelligent systems, CYBER 2023

作者： Liu, Qi Yang, Bo Liu, Yuxiang Ma, Kai Guan, Xinping Shanghai Jiao Tong University Ministry of Education Key Laboratory of System Control and Information Processing Department of Automation Shanghai200240 China School of Electrical Engineering Yanshan University Qinhuangdao066004 China

ISBN: (纸本)9798350315196

Aiming at accurate identification of emerging new faults in PV systems, a novel PV fault diagnosis framework based on cloud-edge collaboration is designed. Model updates are deployed on the cloud servers, while real-time diagnosis is implemented at the edge devices. A multi-label learning model is developed to extract fault features efficiently, and a threshold scheme is further designed for feature enhancement. The obtained features are processed by K-Means clustering and the One-Class Support Vector Machine (OCSVM) methods, which can accurately diagnose existing types and identify unknown faults. For analysis and demonstration, the Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE) methods are used for dimensionality reduction and visualization. Numerical results validate the effectiveness of the proposed method. © 2023 IEEE.

关键词： K-means clustering

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Frequency Regulation Reserve Optimization Considering Flexible Resource Complementary Aggregation

Frequency Regulation Reserve Optimization Considering Flexib...

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Smart Grid and Artificial Intelligence (SGAI), International Conference on

作者： Zirui Huang Wenbo Mao Yaping Li Kaifeng Zhang Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Southeast University Nanjing China China Electric Power Research Institute CO. Ltd Nanjing China

ISBN: (数字)9798331518806

ISBN: (纸本)9798331518813

This paper proposes a fast-solving strategy that uses the complementarity of flexible resources to aggregate and participate in frequency regulation. Firstly, this paper proposes a frequency regulation performance index which can consider the adjustment rate, response delay time, adjustable capacity and adjustment cost of flexible resources. Then, based on the index, this paper uses the K-means algorithm to cluster the resources with similar performance. Then, this paper proposes an aggregation strategy considering the complementary of multiple flexible resources. Finally, this paper improves the unit combination optimization model, so that flexible resources can participate in the optimization in the form of aggregation, and the ratio of resources can be determined in advance, reducing the number of optimization parameters and improve the efficiency of solution. In this paper, the simulation is carried out on the IEEE-118 node. The results show that the proposed scheme can effectively improve the rate of optimization solution and effectively avoid the curse of dimensionality in the face of a large number of flexible resources.

关键词： Costs Optimization models Face recognition Smart grids Performance analysis Delays Time factors Indexes Optimization Frequency control

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Robust control Analysis With Model Transformation for Interval Type-2 Fuzzy systems

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IEEE Transactions on Fuzzy systems 2025年

作者： Yang, Jie Gai, Shaoyan Da, Feipeng Xie, Wenbo Southeast University School of Automation Nanjing210096 China Ministry of Education Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China Shanghai University and Shanghai Artificial Intelligence Laboratory School of Mechatronic Engineering and Automation Shanghai200444 China

To address the problem of robust stability analysis for interval type-2 fuzzy systems (IT2FSs), this paper proposes an innovative analysis approach based on model transformation. Firstly, a classical piecewise linear approximation method is utilized to process the upper boundary membership functions (UMFs) and lower boundary membership functions (LMFs) of the footprint of uncertainty (FOU) in IT2FSs, resulting in linear boundary membership functions (MFs) that are more convenient for analysis, along with the corresponding approximation error functions. Subsequently, a novel error model transformation method is introduced to handle these error terms. By constructing new fuzzy rules and MFs, the boundary error terms are converted into a new fuzzy model, thereby incorporating more information about the error functions into the stability analysis. Based on this model, a robust stability condition in the form of linear matrix inequalities (LMIs) are derived, achieving improved robustness. Finally, the effectiveness of the proposed method is validated through simulations on real-world systems, and its superiority is demonstrated by comparison with existing methods. © 2025 IEEE.

关键词： Membership functions

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Wearable multichannel pulse condition monitoring system based on flexible pressure sensor arrays

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Microsystems & Nanoengineering 2022年第1期8卷 249-258页

作者： Jie Wang Yirun Zhu Zhiyong Wu Yunlin Zhang Jian Lin Tao Chen Huicong Liu Fengxia Wang Lining Sun Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric EngineeringSoochow UniversitySuzhou 215123China Micro Nano System Research Center Key Laboratory of Advanced Transducers and Intelligent Control System of Ministry of Education and Shanxi Province&College of Information EngineeringTaiyuan University of TechnologyTaiyuan 030024China Suzhou Institute of Nano-tech and Nanobionics Chinese Academy of SciencesSuzhou 215123China

Pulse diagnosis is an irreplaceable part of traditional Chinese medical ***,application of the traditional pulse monitoring method was restricted in the modernization of Chinese medical science since it was difficult to capture real signals and integrate obscure feelings with a modern data ***,a novel multichannel pulse monitoring platform based on traditional Chinese medical science pulse theory and wearable electronics was *** pulse sensing platform simultaneously detected pulse conditions at three pulse positions(Chi,Cun,and Guan).These signals were fitted to smooth surfaces to enable 3-dimensional pulse mapping,which vividly revealed the shape of the pulse length and width and compensated for the shortcomings of traditional single-point pulse ***,the pulse sensing system could measure the pulse signals from different individuals with different conditions and distinguish the differences in pulse *** addition,this system could provide full information on the temporal and spatial dimensions of a person’s pulse waveform,which is similar to the true feelings of doctors’*** innovative,cost-effective,easily designed pulse monitoring platform based on flexible pressure sensor arrays may provide novel applications in modernization of Chinese medical science or intelligent health care.

关键词： multichannel system replace

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GPIO Based Iterative Learning control for Manipulators with Time-Varying Disturbances 42

GPIO Based Iterative Learning Control for Manipulators with ...

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42nd Chinese control Conference, CCC 2023

作者： Lu, Huangyuwei Wang, Zuo Sun, Jiankun Xiao, Xi Li, Shihua School of Automation Southeast University Nanjing210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southeast University Shenzhen518063 China School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Aviation Key Laboratory of Science and Technology on Aero Electromechanical System Integration Nanjing211106 China

ISBN: (纸本)9789887581543

To improve the disturbance rejection ability and repetitive tracking accuracy of manipulators, a composite iterative learning control (ILC) scheme via generalized proportional integral observer (GPIO) is proposed. A high-order polynomial is first employed to model the time-varying disturbances that include unmodeled dynamics, parameter uncertainties and load fluctuations. A GPIO is then designed to estimate the disturbances and high-order time derivatives. By introducing estimations into a PD-type ILC at each iteration, a composite control scheme is obtained, such that the undesirable influence of time-varying disturbances can be effectively attenuated in the repetitive tracking process of manipulators. In this paper, the rigorous stability analysis of the closed-loop system is presented to guarantee that the tracking error exponentially converges to zero as the iteration number tends to infinity. The effectiveness of the proposed control scheme is finally verified by simulation results. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Disturbance rejection

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3D Imaging Sensing and Prediction Technology of Large Component Surface with Multi-source Sensing 6

3D Imaging Sensing and Prediction Technology of Large Compon...

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6th Conference on Frontiers in Optical Imaging and Technology: Applications of Imaging Technologies

作者： Sun, Jing Li, Lijuan Lin, Xuezhu Guo, Lili Liu, Yue Yan, Dongming Key Laboratory of Optoelectronic Measurement and Control and Optical Information Transmission Technology Ministry of Education College of Optoelectronic Engineering Changchun University of Science and Technology Changchun130022 China Zhongshan Research Institute of Changchun University of Science and Technology Guangdong Zhongshan528400 China

ISBN: (数字)9781510679757

ISBN: (纸本)9781510679740

In order to solve the problem that the surface deformation of large components cannot be photographed in real time due to the influence of assembly stress during assembly, a multi-source sensor surface deformation sensing and 3D imaging prediction technology for large components is proposed, which combined laser tracking, structured light detection and optical fiber sensing monitoring. Firstly, the multi-source sensor system is analyzed, and the multi-source sensor fusion sensing field is constructed. Secondly, the unified model of heterogeneous data is established to complete the accurate conversion of optical fiber monitoring wavelength to space point coordinates. Thirdly, multi-source data fusion is realized based on Gaussian process fusion algorithm to complete 3D imaging prediction of component surface deformation. Finally, a large skin component is taken as an example to simulate the assembly deformation experiment. The results show that the root-mean-square error is less than 0.1mm compared with the actual results. Based on the measured data of structured light scanning and multi-source sensing technology, a real-time sensing and prediction method for surface deformation imaging of large components is proposed. This method not only simplifies the scanning mode of structured light, but also provides a new idea for dynamic model building. © 2024 SPIE.

关键词： Forecasting

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Parameter-level Mask-based Adapter for 3D Continual Learning with Vision-Language Model

Parameter-level Mask-based Adapter for 3D Continual Learning...

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IEEE International Conference on Robotics and Biomimetics

作者： Longyue Qian Mingxin Zhang Qian Zhang Lin Zhang Teng Li Wei Zhang Key Laboratory of Machine Intelligence and System Control Ministry of Education School of Control Science and Engineering Shandong University Jinan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

As 3D object classification becomes increasingly essential in various applications, the need for models to continuously adapt to new scenarios is more crucial than ever. However, existing continual learning methods primarily concentrate on 2D tasks, making their direct application to 3D tasks challenging due to the inherent domain gap. To address this issue, we propose Parameter-level Mask-based Adapter (PMA) for 3D continual learning with Vision-Language Models. Our approach leverages the power of Contrastive Language-Image Pre-Training (CLIP) to extract rich multimodal features by projecting 3D point clouds into multi-angle depth maps, which are then processed by the 2D encoder aligned with rendered images. The core of our method is a parameter-level adapter with learnable masks that effectively integrates 3D point cloud features with 2D features. This mechanism flexibly selects the most relevant subsets of weights for each task, allowing subsequent tasks to utilize the learned weights without the need for updates, which effectively balances stability and plasticity during continual learning. Experimental results show that our method significantly outperforms existing state-of-the-art techniques.

关键词： Continuing education Point cloud compression Training Representation learning Solid modeling Adaptation models Three-dimensional displays Feature extraction Stability analysis Robots

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P2CBAM: 3D Object Detection for Autonomous Driving

P2CBAM: 3D Object Detection for Autonomous Driving

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IEEE International Conference on Robotics and Biomimetics

作者： Ruitong Li Tao Zhang Xiaoyu Xu Yuenan Zhao Ran Song Wei Zhang Ministry of Education School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Jinan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Efficient and accurate object detection from 3D point clouds is crucial for autonomous driving. Due to the sparsity and irregularity of real-world point clouds, as well as the real-time requirements for detectors, existing 3D object detectors face significant challenges in practical applications. PointPillars is widely used in real-world scene due to its fast detection speed, but its accuracy lags behind some existing methods. To enhance the detection accuracy of this model, we propose P 2 CBAM, which integrates CBAM into the PointPillars baseline model. This integration improves detection accuracy with relatively minimal additional computational resources. Extensive experiments conducted on the KITTI and Waymo Open datasets demonstrate that P 2 CBAM outperforms the baseline model on both datasets. The code is available at https://***/Bennyliiiiii/P2CBAM.

关键词： Point cloud compression Three-dimensional displays Accuracy Computational modeling Detectors Object detection Feature extraction Real-time systems Autonomous vehicles Robots

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Fault detection method of photovoltaic system based on power change of array 4

Fault detection method of photovoltaic system based on power...

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4th International Conference on Algorithms, Computing and Artificial Intelligence, ACAI 2021

作者： Tan, Chao Li, Chenxi Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing210096 China

ISBN: (纸本)9781450385053

The fault detection of photovoltaic power generation system is of great significance in photovoltaic plant management. The conventional fault detection method of photovoltaic power system requires additional sensors, and the fault detection scheme needs to be redesigned if the parameters of the photovoltaic plant change. Therefore, for large photovoltaic plants, it is necessary to develop a method that does not require the use of external sensors or the detailed parameters of the photovoltaic plant. This paper proposes a photovoltaic system fault detection method based on photovoltaic array current and voltage data. This method does not require additional sensor data such as irradiance and temperature. The failure of photovoltaic system can be detected through the change of power, and the abnormal situation of photovoltaic power station can still be detected under cloudy and shaded conditions. The advantages of this method are that there is no need for complex and diverse sensor equipment, the calculation is simple and the fault detection ability is accurate. The experimental data of Padang Poverty Power Station of State Grid has been verified. © 2021 ACM.

关键词： Fault detection

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Learning to Navigate Sequential Environments: A Continual Learning Benchmark for Multi-modal Navigation

Learning to Navigate Sequential Environments: A Continual Le...

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IEEE International Conference on Robotics and Biomimetics

作者： Yinduo Xie Yuenan Zhao Qian Zhang Lin Zhang Teng Li Wei Zhang School of Control Science and Engineering Shandong University Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan China

ISBN: (数字)9798331509644

ISBN: (纸本)9798331509651

Existing works for robot navigation typically focus on performance in specific tasks, overlooking the adaptability of navigation strategies to sequential environments. In this paper, we present CLBMN, a continual learning (CL) benchmark for multi-modal robot navigation, which aims to enhance robots' navigation capabilities in handling sequential tasks. The benchmark is composed of a multi-modal dataset, a CL-based algorithm, and three comprehensive evaluation metrics for CL-based multi-modal navigation. Specifically, we construct a multi-modal dataset with 10,000 samples by collecting images, point clouds, and odometry data from five scenarios, including daytime, nighttime, rainy conditions, highway, and field. Then, we propose an end-to-end navigation framework with momentum update to mitigate catastrophic forgetting across various environments, which provides a baseline approach for CL-based robot navigation. Furthermore, we define three evaluation metrics specifically designed to evaluate the robot's continual navigation performance across different scenarios. Experimental results demonstrate that the proposed benchmark is effective for studying multi-modal continual robot navigation in sequential environments. Our project page is available at https://***/clbmn/.

关键词： measurement Continuing education Road transportation Point cloud compression Navigation Biomimetics Benchmark testing Odometry Robots

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