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A Review of Rapid and Accurate Monitoring, Tracing and Coordinated Suppression of Broadband Oscillations in New Energy Grid-Connected Systems 19th

A Review of Rapid and Accurate Monitoring, Tracing and Coord...

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19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Zhang, Shuai Dai, Tongyin Sun, Zhidong Liu, Chen Feng, Junhao School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9789819613786

Vigorously developing new energy is an important cornerstone for achieving carbon peak and carbon neutrality, and is related to national economic development and energy security. In recent years, with the strong support of the country, my country’s new energy industry has developed rapidly, and its installed capacity has jumped to the first place in the world. However, the large-scale access of new energy has profoundly changed the dynamic characteristics of the power system, causing new problems such as broadband oscillations in new energy grid-connected systems, which seriously threaten the safe and stable operation of the system. This paper first introduces the characteristics of broadband oscillations in new energy grid-connected systems, and summarizes the key scientific issues that need to be solved in the three fundamental tasks of broadband monitoring, precise tracing and stable suppression. Then, starting from the classification of technologies and the limitations of technologies, the current research status of broadband oscillation monitoring, tracing and suppression of new energy grid-connected systems is sorted out and summarized. Finally, the future research and development direction of broadband oscillation problems in new energy grid-connected systems is prospected. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Windmill

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Dual-View Spatio-Temporal Interactive Network for Video Human Action Recognition 17th

Dual-View Spatio-Temporal Interactive Network for Video Huma...

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17th International Conference on Intelligent Robotics and Applications, ICIRA 2024

作者： Wu, Hanbo Ma, Xin School of Control Science and Engineering Shandong University Jinan China

ISBN: (纸本)9789819607761

Capturing discriminative spatiotemporal feature information is crucial for enhancing the performance of video action recognition. Currently, most methods generally embed spatiotemporal feature modeling modules into the backbone network to achieve human action recognition under a single view. However, action video has a 3D structure, and multi-view information helps to acquire more comprehensive body-related clues to improve the robustness and accuracy of action recognition. Depth data is insensitive to lighting and color changes, particularly in providing reliable 3D geometric information of human actions. In this paper, we focus on the task of depth video-based action recognition and propose a Dual-view Spatio-Temporal Interactive Network (DSTIN), which enhances action recognition performance by effectively integrating spatiotemporal interactive features from front and side views. Specifically, the core of the DSTIN is the Dual-view Spatio-Temporal Interactive Module (DSTIM), which operates on convolutional feature maps from two views. It first exchanges spatiotemporal information along the channel dimension to achieve inter-view channel feature interaction, and then applies temporal shift along the time dimension within each view to model motion information. The proposed DSTIM can be flexibly embedded into any 2D deep network architectures to enhance their ability. We choose 2D Resnet50 as the backbone to generate DSTIN, which takes depth dynamic image sequences from two different views as the input and achieves an end-to-end action recognition training framework through inter-view spatiotemporal information interaction and fusion. Extensive experiments on two large-scale RGBD datasets demonstrate that the proposed method significantly improves the performance of video human action recognition. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Spatio-temporal data

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MPGA-YOLOv8: A Leaf Disease Detection Network Combining Multi-Scale Progressive Feature Network and Global Attention

IAENG International Journal of Computer Science

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IAENG International Journal of Computer science 2025年第2期52卷 436-446页

作者： Xu, Jiacheng Yang, Yonghui Chu, Maoxiang Student of School of Electronic Information University of Science and Technology Liaoning Anshan114051 China School of Control Science and Engineering University of Science and Technology Liaoning Anshan114051 China

Considering the diversity of natural conditions, including fog, low light, and strong light, as well as the impact of various diseases on leaves, we propose an improved apple leaf disease detection method based on the YOLOv8n model. This method first uses a Multi-Scale Progressive Feature Network(MSPN) as the neck network to integrate multiple morphological lesions and enhance the model’s information integration capability. Then, a Global Self Attention Module (GSAM) is applied at the end of the backbone to help the model capture spatial relationships in the image and reduce interference from extremely complex background conditions. Next, we replace the convolution blocks in the backbone with Receptive Field-Focused Convolution Block (RFFconv) to effectively improve the model’s recognition accuracy through shared receptive field weights. Finally, we add a small object detection layer for small lesions to enhance the model’s generalization performance. Experimental results show that the proposed MPGA-YOLOv8 model effectively detects six types of apple leaf diseases in complex backgrounds, including healthy samples, with an average mAP accuracy of 74.3%. Compared to classic models like SSD, Faster RCNN, YOLOv3 tiny, YOLOv5, and YOLOv6, the mAP performance improves by 20.9%, 38.4%, 9.8%, 10.0%, and 14.3%, respectively. This model enables rapid and accurate detection and recognition of apple leaf diseases, providing viable technical support and solutions for disease prevention and control. © (2025), (International Association of Engineers). All rights reserved.

关键词： Disease control

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An Improved Current-Sensorless Model Predictive Voltage control for Four-Leg Voltage Source Inverters 14

An Improved Current-Sensorless Model Predictive Voltage Cont...

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14th Annual IEEE Applied Power Electronics Conference and Exposition, APEC 2025

作者： Guo, Heng Wei, Yuxin Jing, Mengmeng Ding, Wenlong Duan, Bin Zhang, Chenghui Shandong University School of Control Science and Engineering Jinan China

ISBN: (纸本)9798331516116

Four-Leg voltage source inverters (VSIs) with LC output filter are commonly utilized to stabilize three-phase sinusoidal voltages under unbalanced loads conditions. However, conventional model predictive voltage control (MMPVC) for four-leg VSIs relies heavily on current sensors, leading to increased costs and suboptimal output voltage performance. To address these issues, this paper proposes an improved current-sensorless modulated model predictive voltage control (MMPVC) for four-leg VSIs to effectively reduce voltage tracking errors and total harmonic distortion (THDv). First, a readily implementable capacitor current extended state observer is constructed to replace current sensors and a corresponding discrete model is reconstructed. Then, four-vector MMPVC for four-leg VSIs is carefully designed. Especially, the ineffective voltage vectors are found, and their duration time is set as zero to optimize the output voltage performance in edge regions of sectors and over-modulation regions. Finally, experimental results validate the proposed approach, demonstrating substantial improvements in output voltage quality and tracking accuracy in over-modulation regions. © 2025 IEEE.

关键词： Model predictive control

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Research on multi-scale simulation and dynamic verification of high dynamic MEMS components in additive manufacturing

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Defence Technology 2025年 47卷 275-291页

作者： Lv, Sining Feng, Hengzhen Lou, Wenzhong Xiao, Chuan Li, Shiyi Science and Technology on Electromechanical Dynamic Control Laboratory School of Mechatronical Engineering Beijing Institute of Technology Beijing100081 China

Metal Additive Manufacturing (MAM) technology has become an important means of rapid prototyping precision manufacturing of special high dynamic heterogeneous complex parts. In response to the micro-mechanical defects such as porosity issues, significant deformation, surface cracks, and challenging control of surface morphology encountered during the selective laser melting (SLM) additive manufacturing (AM) process of specialized Micro Electromechanical System (MEMS) components, multi-parameter optimization and micro powder melt pool/macro-scale mechanical properties control simulation of specialized components are conducted. The optimal parameters obtained through high-precision preparation and machining of components and static/high dynamic verification are: laser power of 110 W, laser speed of 600 mm/s, laser diameter of 75 μm, and scanning spacing of 50 μm. The density of the subordinate components under this reference can reach 99.15%, the surface hardness can reach 51.9 HRA, the yield strength can reach 550 MPa, the maximum machining error of the components is 4.73%, and the average surface roughness is 0.45 μm. Through dynamic hammering and high dynamic firing verification, SLM components meet the requirements for overload resistance. The results have proven that MEM technology can provide a new means for the processing of MEMS components applied in high dynamic environments. The parameters obtained in the conclusion can provide a design basis for the additive preparation of MEMS components. © 2024 China Ordnance Society

关键词： Microchannels

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Depth Completion With Super-Resolution and Cross-Modality Optimization

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IEEE Robotics and Automation Letters 2025年第6期10卷 5585-5592页

作者： Zhong, Ju Jiang, Aimin Liu, Chang Xu, Ning Zhu, Yanping Hohai University College of Information Science and Engineering Changzhou210098 China Changzhou University School of Microelectronics and Control Engineering Changzhou213164 China

Depth completion, the process of generating dense depth maps from sparse or incomplete data, is inherently challenging due to the variability in sensor types, environmental conditions, and the differences between data modalities. In this letter, we propose a novel framework for depth completion. First, we introduce an efficient depth estimation network capable of predicting relative depth from a single RGB image. Next, we design a depth super-resolution network that refines the predicted depth by using Fast Fourier Convolution (FFC) and Gradient-weighted Symmetric Feature Transmission (GSFT) modules. These modules upsample the depth map using high-resolution RGB guidance, effectively mitigating the cross-modality gap. Finally, a global optimization step fuses the upsampled depth with sparse ground truth to produce high-quality dense depth maps. Our unified approach enhances generalization across diverse datasets while avoiding overfitting to specific depth corruption patterns. The enhancement in depth resolution and accuracy are critical for robotic applications requiring precise spatial perception, such as localization and manipulation. Experimental results on NYU-Depth V2 and SUN RGB-D benchmarks demonstrate the superiority of our approach compared to state-of-the-art approaches. © 2016 IEEE.

关键词： Penetration depth (superconductivity)

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Snapshot multispectral polarization imager using a narrowband reflection filter array

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Applied Optics 2025年第12期64卷 3109-3121页

作者： Wu, Wenxuan Yang, Yu Li, Liansheng Zhu, Haibo Huang, Huitong Liu, Yixuan Zong, Yi Li, Jianxin School of Electronic and Optical Engineering Nanjing University of Science and Technology Nanjing210094 China Beijing Institute of Control Engineering Beijing100190 China

Multispectral polarization imaging technology holds significance in various fields. This study proposes a multispectral polarization imaging scheme based on a narrowband reflection filter array (RFA), incorporating spatial dispersion that leverages the RFA to segment optical paths for multispectral image acquisition. The RFA was analyzed and designed, and a snapshot multispectral polarization imager was constructed. The feasibility of the spatial dispersion structure was experimentally validated. The proposed method can capture spectral polarization images at video frame rates with relatively consistent imaging quality across different bands and high spatial resolution, providing, to our knowledge, a new technical approach for realizing real-time multispectral polarization imagers. © 2025 Optica Publishing Group. All rights, including for text and data mining (TDM), Artificial Intelligence (AI) training, and similar technologies, are reserved.

关键词： Image acquisition

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Neural Network for Distributed Collaboration of Multiple Manipulators With Switching Topologies: A Game-Theoretic Perspective

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IEEE Transactions on Systems, Man, and Cybernetics: Systems 2025年第5期55卷 3213-3221页

作者： Liu, Mei Mu, Wenxin Lv, Xin Sun, Zhongbo Jin, Long Lanzhou University School of Information Science and Engineering Lanzhou730000 China Changchun University of Technology Department of Control Engineering Changchun130000 China

Since distributed control strategies can effectively reduce the operating load of the central processor, they have become a prominent research direction in the field of controlling multiple manipulators. However, existing distributed approaches predominantly rely on fixed topologies, overlooking the dynamic nature of task requirements. To address this limitation, this article proposes a distributed collaboration scheme that incorporates switching topologies. The distributed control problem is further formulated as a game-theoretic framework involving multiple manipulators. A dynamic neural network solver is then developed to approximate the optimal Nash equilibrium strategy, with its convergence and stability proven through theoretical analysis. The effectiveness of the proposed scheme and solver is validated through a series of physical experiments. © 2013 IEEE.

关键词： Redundant manipulators

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Incomplete data modeling based on alternate update of clustering and autoencoder for missing value imputation

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Neural Computing and Applications 2025年第3期37卷 1523-1540页

作者： Lai, Xiaochen Zhang, Zheng Zhang, Liyong Lu, Wei Li, ZhuoHan School of Software Dalian University of Technology Dalian116600 China School of Control Science and Engineering Dalian University of Technology Dalian116024 China

Missing values exist widely in real-world datasets, which restrict the performance of data mining. In this paper, we propose a joint optimization framework to mine attribute associations and category structures in incomplete datasets, aiming to impute missing values with a full understanding of the data structure. Considering the differences in attribute correlations among different sample categories, we partition incomplete data into fuzzy subsets by fuzzy clustering. Within each subset, a tracking-removed autoencoder is constructed as a submodel to fit the regression relationships among attributes. Due to the mutual influence between fuzzy clustering and regression modeling, we further propose a missing value variable-based training scheme to iteratively optimize these two processes. Our proposed framework offers the advantage of decomposing the complex imputation task into simpler sub-tasks by fuzzy clustering where the attribute associations are more explicit. Moreover, the proposed training scheme activates the complementary nature of clustering and regression processes to reduce imputation errors. The experimental results on artificial and real datasets illustrate the effectiveness of our proposed framework. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Fuzzy clustering

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Design and Experimental Study of Special Elastic Leg Joint for Quadruped Robots

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Journal of Bionic engineering 2025年第2期22卷 642-653页

作者： Zisen Hua Chi Chen Xuewen Rong Yibin Li School of Artificial Intelligence Anhui University of Science&TechnologyTaifeng RoadHuainan232001Anhui ProvinceChina School of Control Science and Engineering Shandong UniversityJingshi RoadJinan17951Shandong ProvinceChina

In this paper,a novel passive flexible leg joint method is proposed with the aim of enhancing the impact buffering capability as well as reducing energy *** innovative structure cleverly incorporates micro-plate springs,offering significant stiffness adjustment *** meet the stiffness requirements,the relationships between foot-ground contact force and the deformation force of the elastic component,as well as the influence of elastic component deformation and foot cushioning amplitude are comprehensively *** the aid of finite element optimization analysis,a single-leg experimental platform is designed,and the effectiveness and applicability of the novel structure are validated through experiments including unloaded free swinging,freely falling body motion and ground squats *** experiments results show the evident superiorities of the passive compliance joint.

关键词： Quadruped robot Passive compliance Leg joint Motion energy efficiency

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