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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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Identifiability Analysis of Load Model Parameters by Estimating Confidential Intervals

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CSEE Journal of Power and Energy systems 2023年第5期9卷 1666-1675页

作者： Xinran Zhang Chao Lu Ying Wang Qiantu Ruan Hongbo Ye Weihong Wang the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China. the State Key Laboratory of Control and Simulation of Power System and Generation Equipment Tsinghua UniversityBeijing 100084China School of Technology Beijing Forestry UniversityBeijing 100083China the State Grid Shanghai Electric Power Company Shanghai 200122China

The identification of load model parameters from practical measurement data has become an essential method to build load models for power system simulation,analysis and *** practical situations,the accuracy of the load model parameters identification results is impacted by data quality and measurement accuracy,which leads to the problem of *** this paper,an identifiability analysis methodology of load model parameters,by estimating the confidential intervals(CIs)of the parameters,is *** load model structure and the combined optimization and regression method to identify the parameters are first ***,the definition and analysis method of identifiability are *** CIs of the parameters are estimated through the profile likelihood method,based on which a practical identifiability index(PII)is defined to quantitatively evaluate ***,the effectiveness of the proposed analysis approach is validated by the case study results in a practical provincial power *** results show that the impact of various disturbance magnitudes,measurement errors and data length can all be reflected by the proposed ***,the proposed PII can provide guidance in data length selection in practical load model identification situations.

关键词： Confidential interval Load modeling parameter estimation practical identifiability

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Formation control of Second-Order Nonholonomic systems with a Novel Model-Based Event-Triggered Mechanism 7th

Formation Control of Second-Order Nonholonomic Systems with...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Xiao, Feng Sun, Sijia Wang, Xiaoyu State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power University Beijing China School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9789819733392

The formation control problem of second-order nonholonomic systems formulated by a force-balance equation is investigated with network communication constraints. A novel event-triggered mechanism based on polynomial interpolation predictive models is presented. To address the stabilization obstacles imposed by the nonholonomic constraints, persistently exciting (PE) functions are utilized to design the distributed control protocols. Under the designed event-triggered mechanism, the system does not exhibit Zeno behavior. Sufficient conditions for second-order nonholonomic systems to achieve stable formation are obtained based on Barbalat’s lemma. Finally, numerical examples are provided to demonstrate the effectiveness of the proposed results. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Distributed parameter control systems

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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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SCChOA:Hybrid Sine-Cosine Chimp Optimization Algorithm for Feature Selection

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Computers, Materials & Continua 2023年第12期77卷 3057-3075页

作者： Shanshan Wang Quan Yuan Weiwei Tan Tengfei Yang Liang Zeng School of Electrical and Electronic Engineering Hubei University of TechnologyWuhan430068China Hubei Key Laboratory for High-Efficiency Utilization of Solar Energy and Operation Control of Energy Storage System Hubei University of TechnologyWuhan430068China Xiangyang Industrial Institute of Hubei University of Technology Xiangyang441100China

Feature Selection(FS)is an important problem that involves selecting the most informative subset of features from a dataset to improve classification ***,due to the high dimensionality and complexity of the dataset,most optimization algorithms for feature selection suffer from a balance issue during the search ***,the present paper proposes a hybrid Sine-Cosine Chimp Optimization Algorithm(SCChOA)to address the feature selection *** this approach,firstly,a multi-cycle iterative strategy is designed to better combine the Sine-Cosine Algorithm(SCA)and the Chimp Optimization Algorithm(ChOA),enabling a more effective search in the objective ***,an S-shaped transfer function is introduced to perform binary transformation on ***,the binary SCChOA is combined with the K-Nearest Neighbor(KNN)classifier to form a novel binary hybrid wrapper feature selection *** evaluate the performance of the proposed method,16 datasets from different dimensions of the UCI repository along with four evaluation metrics of average fitness value,average classification accuracy,average feature selection number,and average running time are ***,seven state-of-the-art metaheuristic algorithms for solving the feature selection problem are chosen for *** results demonstrate that the proposed method outperforms other compared algorithms in solving the feature selection *** is capable of maximizing the reduction in the number of selected features while maintaining a high classification ***,the results of statistical tests also confirm the significant effectiveness of this method.

关键词： Metaheuristics chimp optimization algorithm sine-cosine algorithm feature selection and classification

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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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Enhanced precipitation responses over the Tibetan Plateau following future Tambora-size volcanic eruption

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Advances in Climate Change Research 2024年第5期15卷 845-858页

作者： Xin-Jun GAN Lin-Shan YANG Meng ZUO Fei LIU Chao-Chao GAO College of Environmental and Resource Science Zhejiang UniversityHangzhou 310058China Zhejiang Provincial Key Laboratory of Organic Pollution Process and Control Hangzhou 310058China State Key Laboratory of Severe Weather and Institute of Tibetan Plateau Meteorology Chinese Academy of Meteorological SciencesBeijing 100081China School of Atmospheric Sciences&Key Laboratory of Tropical Atmosphere Ocean System Ministry of Education Sun Yat-sen UniversiyZhuhai 519082China Southern Marine Science and Engineering Guangdong Laboratory Sun Yat-sen UniversityZhuhai 519082China

Hydroclimate over the Tibetan Plateau(TP)notably influences the eco-environment of the Northern *** its high elevation and complex topography,the climate in the TP shows a high sensitivity to anthropogenic warming and volcanic-induced *** mechanism by which a future volcanic or similar radiative perturbation affects precipitation in the TP under an anthropogenic warming climate must be addressed not only to enable regional adaptation but deepen our understanding of how a climate system evolves under such a dual ***,based on the Community Earth system Model version 1.2 and ensemble simulations under pre-industrial and RCP8.5 scenarios,we showed that a Tambora-sized volcanic perturbation led to severe rainfall reduction over the south TP in the following summer(June-August).Evaporation response accounted for a minor and relatively constant share of precipitation reduction following the Clausius-Clapeyron scaling,whereas dynamic processes triggered an El Nino-like response in the eastern equatorial Pacific,which suppressed the Walker and Hadley circulation and contributed to drying *** warming renders the post-Tambora hydroclimate responses with 30%higher severity as a result of the increased climatological moisture content and intensified El Nino response,which enhanced hydroclimate sensitivity and attenuated monsoon *** results illustrate the amplification effect of global warming on the plateau's hydroclimate responses to external forcings,which may add another layer of uncertainty on climate adaptation in this already complex region.

关键词： Tibetan Plateau The community earth system model Hydrological response Tambora eruption El Nino

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Digital-Twin-Based Clock Synchronization in Industrial IoT systems 13

Digital-Twin-Based Clock Synchronization in Industrial IoT S...

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

作者： Cheng, Ting Liu, Shuai Xu, Qimin Zhu, Shanying Department of Automation Shanghai Jiao Tong University Shanghai200240 China Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai200240 China Shanghai Engineering Research Center of Intelligent Control and Management Shanghai200240 China School of Control Science and Engineering Shandong University Jinan250061 China

ISBN: (纸本)9798350315196

Close and efficient cooperation between devices in the Industrial Internet of Things (IIoT) requires precise clock synchronization as a prerequisite. The uncertainty of IIoT networks and the complexity of industrial environments have led to less precise clock synchronization achieved by traditional algorithms and higher network resource consumption. This paper proposes a digital-twin-based clock synchronization scheme for IIoT systems. By building the digital model for each local clock to predict their clock skew, the information interaction required to achieve synchronization is reduced. In order to model the clock accurately, this paper proposes to use the extended state Kalman filter to handle uncertain dynamics and unknown measurement bias to improve the estimation accuracy of clock skew further. The simulation results show that the proposed algorithm can achieve high- precision clock synchronization while reducing the consumption of network resources. © 2023 IEEE.

关键词： Internet of things

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The Neural Network Algorithm for Decision Support During control of the Welding Process by Robotic Complexes 15

The Neural Network Algorithm for Decision Support During Con...

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15th International Conference Management of Large-Scale system Development, MLSD 2022

作者： Fominykh, Dmitry Fuchedzhi, Nikolai Kushnikov, Oleg Laboratory of System Problems of Control and Automation in Mechanical Engineering Institute for Precision Mechanics and Control Problems of Ras Saratov Russia V.A. Trapeznikov Institute of Control Sciences of Ras Moscow Russia

ISBN: (数字)9781665497015

ISBN: (纸本)9781665497015

The article proposes an algorithm for solving the problem of controlling the welding process in robotic technological complexes according to the quality criterion of the products. The statement of the problem is given and the model of system dynamics by J. Forrester is considered. Welding quality indicators in robotic complexes were used as system levels. Functional dependencies are taken by using the mathematical apparatus of artificial neural networks, in particular, radial basis functions that allow for rigid interpolation. The introduction of the models and algorithms discussed in the article will improve the quality of products using robotic technological complexes. © 2022 IEEE.

关键词： Robotics

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Cooperative Prediction Approach for the Small-Signal Stability Margin of Renewable Power system Based on Vertical Federated Learning 3

Cooperative Prediction Approach for the Small-Signal Stabili...

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3rd International Conference on Power systems and Electrical Technology, PSET 2024

作者： Lai, Xiang Chen, Zhihao Liu, Yun Dai, Tianchen Cai, Haiqing Gu, Haohan Chen, Wei South China University of Technology School of Electric Power Engineering Guangzhou China CSG Electric Power Research Institute State Key Laboratory of HVDC Guangzhou China Laboratory of Intelligent Operation and Control for New Energy Power System Guangdong Provincial Key Guangzhou China

ISBN: (纸本)9798350353204

Since sub-synchronous oscillations have been frequently witnessed in large-scale wind farms under weak grid conditions, it is significant to predict the critical stability situations in short term so that timely preventative actions can be taken to avoid potential small-signal stability (SS) risks. To this end, this paper proposes a SS prediction approach based on the grid strength metric of generalized short-circuit ratio (gSCR) under the mechanism of vertical federated learning. Specifically, the improved ridge regression prediction model is employed and its corresponding distributed training and the distributed predicting are derived by utilizing the separable characteristic of gradient vector calculation in the context of federated learning. On this basis, the original largescale predicting problem is decomposed into a large number of small-scale subproblems, and each subproblem is calculated locally by the corresponding wind farm. The feature data information of adjacent wind farms can be effectively used to improve the accuracy of the prediction of the stability margin while ensuring the data privacy and security of all participants. Finally, simulation results illustrate the effectiveness and efficiency of the proposed approach under various scenarios. © 2024 IEEE.

关键词： Federated learning

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