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Automatic Control of Magnetic Helical Microrobots Docking with Target Objects in Liquid Environments

Journal of Bionic Engineering 2025年第2期22卷 574-584页

作者： Fu Zhao Haoran Rong Lefeng Wang State Key Laboratory of Robotics and Systems Harbin Institute of TechnologyHarbin150080China Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of EducationHarbin University of Science and TechnologyHarbin150080China

Bio-inspired magnetic helical microrobots have great potential for biomedical and micromanipulation applications. Precise interaction with objects in liquid environments is an important prerequisite and challenge for helical microrobots to perform various tasks. In this study, an automatic control method is proposed to realize the axial docking of helical microrobots with arbitrarily placed cylindrical objects in liquid environments. The docking process is divided into ascent, approach, alignment, and insertion stages. First, a 3D docking path is planned according to the positions and orientations of the microrobot and the target object. Second, a steering-based 3D path-following controller guides the helical microrobot to rise away from the container bottom and approach the target along the path. Third, based on path design with gravity compensation and steering output limits, alignment of position and orientation can be accomplished simultaneously. Finally, the helical microrobot completes the docking under the rotating magnetic field along the target orientation. Experiments verified the automatic docking of the helical microrobot with static targets, including connecting with micro-shafts and inserting into micro-tubes. The object grasping of a reconfigurable helical microrobot aided by 3D automatic docking was also demonstrated. This method enables precise docking of helical microrobots with objects, which might be used for capture and sampling, in vivo navigation control, and functional assembly of microrobots.

关键词： Magnetic actuation Helical microrobots Visual servo Motion control Docking

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Stability and Stabilization of Sampled-Data Based LFC for Power Systems:A Data-Driven Method

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the School of Automation China University of Geosciences the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

关键词： Sampled data control systems

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A Lightweight YOLO-Based Model in Small Object Detection for UAV Optical Sensors

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IEEE Sensors Journal 2025年第10期25卷 17585-17599页

作者： Sun, Hao-Ran Shi, Bao-Jun Hu, Yun-Long Hebei University of Technology State Key Laboratory of Reliability and Intelligence of Electrical Equipment The Key Laboratory of Advanced Intelligent Protective Equipment Technology Ministry of Education School of Mechanical Engineering Tianjin300401 China

Detecting small objects within extensive remote sensing images captured by unmanned aerial vehicle (UAV) optical sensors is crucial for a variety of military and civilian purposes, but it poses considerable challenges. To address the difficulties associated with the numerous parameters and the high computational requirements of existing object detection models, as well as the inadequate performance in identifying small objects in remote sensing images (RSIs) captured by UAV optical sensors, this paper proposes a lightweight model for small object detection. Firstly, Focal Scalable Intersection over Union Loss (SIoU) loss derived by integrating the concept of Focal loss into the traditional SIoU loss. Secondly, this study utilizes channel attention (CA) and GhostNet to reconstruct the backbone. A layer specifically designed for detecting small objects is incorporated into YOLOv5s utilized a depth residual network neck. Furthermore, this study establishes a small fire dataset and evaluate this model on the small fire, AI-TOD dataset and DOTA dataset. Experimental results show that the proposed model achieves the lightweight goal while maintaining high detection performance for small objects. © 2025 IEEE.

关键词： Optical remote sensing

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Enhancing Graph Structure Learning via Motif-Driven Hypergraph Construction

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IEEE Transactions on Network Science and Engineering 2025年第3期12卷 2333-2344页

作者： Zhao, Jia-Le Zhang, Xian-Jie Ding, Xiao Zhang, Xingyi Zhang, Hai-Feng Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Mathematical Science Hefei230601 China Anhui University Key Laboratory of Intelligent Computing and Signal Processing of Ministry of Education School of Computer Science and Technology Hefei230601 China

Graph neural networks (GNNs), as a cutting-edge technology in deep learning, perform particularly well in various tasks that process graph structure data. However, their foundation on pairwise graphs often limits their capacity to capture latent higher-order topological semantic information. Thus, it is crucial to find a way to extract the latent higher-order information of graphs without missing the lower-order information of the original graph. To address this issue, we here develop a method to construct hypergraph based on motifs, and then a novel neural network framework, named MD-HGNN, is proposed for enhanced graph learning. Specifically, we first utilize motifs of the original graph to construct the hypergraph and eliminate nested structures within the hypergraph to prevent information redundancy. Subsequently, GNNs and hypergraph neural networks (HGNNs) are employed separately to extract the lower-order and higher-order topological semantic information of the graph. Finally, the lower-order and higher-order information are integrated to obtain an embedded representation of graph. Extensive experimental results demonstrate that MD-HGNN preserves the original lower-order graph structure information while effectively extracting higher-order features. Moreover, its performance and robustness are validated across different downstream tasks. © 2013 IEEE.

关键词： Graph neural networks

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An improved efficient adaptive method for large-scale multiexplosives explosion simulations

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Defence technology(防务技术) 2025年第3期45卷 28-47页

作者： Tao Li Cheng Wang Baojun Shi School of Mechanical Engineering Key Laboratory of Advanced Intelligent Protective Equipment TechnologyMinistry of EducationHebei University of TechnologyTianjin300401China State Key Lab of Explosion Science and Safety Protection Beijing Institute of TechnologyBeijing 100081China

Shock wave caused by a sudden release of high-energy,such as explosion and blast,usually affects a significant range of *** utilization of a uniform fine mesh to capture sharp shock wave and to obtain precise results is inefficient in terms of computational *** is particularly evident when large-scale fluid field simulations are conducted with significant differences in computational domain *** this work,a variable-domain-size adaptive mesh enlargement(vAME)method is developed based on the proposed adaptive mesh enlargement(AME)method for modeling multi-explosives explosion *** vAME method reduces the division of numerous empty areas or unnecessary computational domains by adaptively suspending enlargement operation in one or two directions,rather than in all directions as in AME method.A series of numerical tests via AME and vAME with varying nonintegral enlargement ratios and different mesh numbers are simulated to verify the efficiency and order of *** estimate of speedup ratio is analyzed for further efficiency *** large-scale near-ground explosion experiments with single/multiple explosives are performed to analyze the shock wave superposition formed by the incident wave,reflected wave,and Mach ***,the vAME method is employed to validate the accuracy,as well as to investigate the performance of the fluid field and shock wave propagation,considering explosive quantities ranging from 1 to 5 while maintaining a constant total *** results show a satisfactory correlation between the overpressure versus time curves for experiments and numerical *** vAME method yields a competitive efficiency,increasing the computational speed to 3.0 and approximately 120,000 times in comparison to AME and the fully fine mesh method,*** indicates that the vAME method reduces the computational cost with minimal impact on the results for such large-scale high-energy release problems with signi

关键词： Large-scale explosion Shock wave Adaptive method Fluid field simulations Efficient method

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Engineering Acidic Deoxyribozyme with Strongly Cooperative H+ Binding for Subcellular Imaging

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Analytical Chemistry 2025年第17期97卷 9378-9385页

作者： Jia, Pan Meng, Qihan Zhang, Qiang Zhang, Rui Zhang, Zijie Li, Jiuxing Wang, Xin Liu, Yanbiao Chang, Yangyang Liu, Meng School of Environmental Science and Technology Key Laboratory of Industrial Ecology and Environmental Engineering Ministry of Education Dalian University of Technology Dalian POCT Laboratory Dalian116024 China School of Bioengineering Key Laboratory of Bio-Intelligent Manufacturing Ministry of Education Dalian University of Technology Dalian116024 China

Engineering deoxyribozymes under extreme conditions will expand their repertoires for unique applications. Although all known deoxyribozyme-based biosensors were derived to function under or near physiological conditions, no prior study has been conducted to purposely build them to absolutely function under atypical conditions. Here, we reported the design and characterization of an acidic RNA-cleaving deoxyribozyme, termed ET46, that displays strongly cooperative H+ binding for performing catalysis at acidic pH, yielding a Hill coefficient of 5.4. This feature of cooperativity significantly improves the responsiveness of ET46 to small pH variations within the pH range of 4.5-5.5. We further exploited the pH-sensitive properties of ET46 to track pH changes on the uptake and localization in lysosomes. Finally, we developed a lysosomal ATP-activated fluorescence-signaling aptazyme biosensor (named FabATP) using acidic ET46 and a known ATP-binding DNA aptamer. To demonstrate its ability to function in lysosomes, we applied FabATP in live subcellular fluorescence imaging of lysosomal ATP fluctuations upon stimulations. © 2025 American Chemical Society.

关键词： Fractography

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Fractal autoencoder with redundancy regularization for unsupervised feature selection

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Science China(Information Sciences) 2025年第2期68卷 89-102页

作者： Meiting SUN Fangyu LI Honggui HAN School of Information Science and Technology Beijing University of Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Digital Community Ministry of Education Beijing Institute of Artificial Intelligence

Feature selection is a crucial step in data preprocessing because feature selection reduces the dimensionality of data by eliminating irrelevant and redundant features. Since manual labeling is expensive, unsupervised feature selection has received increasing attention in recent years. However, existing unsupervised feature selection methods tend to prioritize selecting highly correlated features over exploring feature diversity. Thus, a regularized fractal autoencoder(RFAE) method is proposed to select informative features in an unsupervised way. Specifically, the fractal autoencoder network extends autoencoders to construct a correspondence neural network and a selection neural network. The correspondence neural network exploits interfeature correlations and the selection neural network selects the informative features. A redundancy regularization strategy consists of a redundancy elimination regularization term based on the dependency between features and a sparse regularization term based on the group lasso. The redundancy regularization strategy eliminates feature subset redundancy and enhances network generalization ability. Extensive experimental results on six publicly available datasets show that the proposed RFAE outperforms the compared methods regarding clustering accuracy and classification accuracy. Moreover, the proposed RFAE achieves acceptable computation efficiency.

关键词： unsupervised feature selection fractal autoencoder correspondence neural network selection neural network redundancy regularization strategy

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An Efficient Instance Segmentation Based on Layer Aggregation and Lightweight Convolution

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Computers, Materials & Continua 2025年第4期83卷 1041-1055页

作者： Hui Jin Shuaiqi Xu Chengyi Duan Ruixue He Ji Zhang College of Mechanical Engineering Chongqing University of TechnologyChongqing400054China Robot and Intelligent Manufacturing Technology Key Laboratory of Chongqing Education Commission of ChinaChongqing400054China

Instance segmentation is crucial in various domains,such as autonomous driving and ***,there is scope for improvement in the detection speed of instance-segmentation algorithms for edge ***,it is essential to enhance detection speed while maintaining high *** this study,we propose you only look once-layer fusion(YOLO-LF),a lightweight instance segmentation method specifically designed to optimize the speed of instance segmentation for autonomous driving *** on the You Only Look Once version 8 nano(YOLOv8n)framework,we introduce a lightweight convolutional module and design a lightweight layer aggrega-tion module called Reparameterization convolution and Partial convolution Efficient Layer Aggregation Networks(RPELAN).This module effectively reduces the impact of redundant information generated by traditional convolutional stacking on the network size and detection speed while enhancing the capability to process feature *** experimentally verified that our generalized one-stage detection network lightweight method based on Grouped Spatial Convolution(GSconv)enhances the detection speed while maintaining accuracy across various state-of-the-art(SOTA)*** experiments conducted on the publicly available Cityscapes dataset demonstrated that YOLO-LF maintained the same accuracy as yolov8n(mAP@0.537.9%),the model volume decreased by 14.3%from 3.259 to=2.804 M,and the Frames Per Second(FPS)increased by 14.48%from 57.47 to 65.79 compared with YOLOv8n,thereby demonstrating its potential for real-time instance segmentation on edge devices.

关键词： Automatic driving convolution deep learning real-time instance segmentation

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CO adsorption, activation, and oxidation on CeO_(2)(111)-supported Fe model catalyst surfaces

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Nano Research 2025年第2期18卷 111-119页

作者： Xingwang Cheng Yi Tu Dongling Zhang Dong Han Luchao Huang Jun Hu Honghe Ding Qian Xu Junfa Zhu National Synchrotron Radiation Laboratory University of Science and Technology of ChinaHefei 230029China Department of Chemical Physics Key Laboratory of Surface and Interface Chemistry and Energy Catalysis of Anhui Higher Education Institutesand Key Laboratory of Precision and Intelligent ChemistryUniversity of Science and Technology of ChinaHefei 230026China

Elucidating the synergistic effect of Fe on CeO_(2) is challenging in CO-related reactions but attractive owing to the improvement in the oxygen storage/release capacity of ceria with the addition of ***,using CeO_(2)(111)-supported Fe model catalysts,CO adsorption,activation,and oxidation on catalyst surfaces was carefully investigated using synchrotron radiation photoemission spectroscopy(SRPES),temperature-programmed desorption(TPD),and infrared reflection absorption spectroscopy(IRRAS).The precursorπ-bonding state for CO dissociative adsorption has been identified through unusually low CO vibration frequencies and a low 0 ML 0.6 ML Top sites Bridge sites MS intensity 1.0 MLπ-bond state 2.0 ML Fe 3p hv=180 eV 100 L CO on 1.6 ML Fe/CeO_(2)(111)54.7 eV Fe^(2+)53.3 eV Fe 01142 cm−1 Recombination desorption Intensity(a.u.)Clean 100 L 150 K 200 K 250 K 300 K 400 K 600 K 100200300400500600700 Temperature(K)6058565452 Binding energy(eV)50 dissociation temperature on Fe/CeO_(2)(111)*** is oxidized by dissociated atomic O followed by the Langmuir–Hinshelwood mechanism,whereas the lattice oxygen of CeO_(2) exhibits low *** CO_(2) yield displays a volcanic curve as a function of Fe *** the 0.6 ML Fe/CeO_(2) surface,weakly bound atomic O on Fe^(2+)results in the best catalytic *** on high Fe coverage surfaces,the CO_(2) yield is limited due to the capture of atomic O by *** results provide comprehensive insights into the adsorption,activation,and oxidation of CO on Fe/CeO_(2) and identify the reaction mechanism,and the active site,which provides deeper insights into CO-related reaction mechanisms over CeO_(2) supported Fe catalysts.

关键词： iron/ceria model catalyst adsorption activation oxidation

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Macro-and micro-mechanical response and damage mechanism of sandstone under high-temperature conditions

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International Journal of Mining Science and technology 2025年第2期35卷 265-274页

作者： Laiwei Wu Yanli Huang Junmeng Li Guiyuan Wang Yingshun Li Xiaotong Li Junzhi Chen Chuning Ji State Key Laboratory of Coal Exploration and Intelligent Mining School of MinesChina University of Mining and TechnologyXuzhou 221116China Key Laboratory of Xinjiang Coal Resources Green Mining Ministry of EducationXinjiang Institute of EngineeringUrumqi 830023China

The thermal effects of coal combustion considerably influence the physical and chemical properties,structural characteristics, and stability of rocks, posing a serious threat to the safety of coal mining operations. In this study, the impacts of temperature on the physical and chemical characteristics(i.e., mineral phase, microstructure, and mechanical strength) of sandstone were investigated by employing experimental methods, including microstructural analysis, uniaxial acoustic emission(AE), and nuclear magnetic resonance(NMR). The results indicate that temperature alters the mineral phase and the pore characteristics, and these two factors jointly affect the mechanical properties of sandstone. The influence of temperature on the mechanical strength of sandstone is categorized into low-temperature strengthening and high-temperature damage, with a threshold temperature identified at 600 ℃. The lowtemperature strengthening effect encompasses both pore strengthening and mineral phase strengthening, while the high-temperature damage effect primarily results from pore damage. As the experimental temperature rises, both the number of AE events and the AE energy transition from a surge in the postpeak failure stage to a stepwise increase during the loading process. This transition implies that the failure mode of the sandstone sample evolves from brittle failure to tensile failure.

关键词： Thermal effects Mineral phase Pore structure Acoustic emission energy Microscopic morphology

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