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5th International Conference on Vibration and Energy Harvesting Applications, VEH 2024

作者： Tang, Tianyi Li, Yunfei Sun, Lining Liu, Huicong State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin China School of Mechanical and Electrical Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou China

ISBN: (纸本)9789819611904

Ocean wave energy is a widely anticipated new type of renewable energy. However, due to the ultra-low-frequency and random motion characteristics of ocean waves, effectively harnessing it presents a significant challenge. This paper proposes a Mag-Boost mechanism based on the phenomenon that the magnetic torque undergoes a sudden change when the magnetic poles of two magnets are orthogonal. This innovative mechanism, requiring just a trigger below 1 Hz, can stimulate a power-magnet to generate high-frequency oscillation without direct contact. Utilizing this mechanism, a high-power wave energy harvester (WEH) with ultra-low-frequency response capabilities has been developed. Tested on a six-degree-of-freedom platform, the WEH demonstrated a high-output power of 1.44W and a power density of 1.2 kW/m3 at a frequency of 1 Hz and an inclination angle of 30°. The peak open-circuit voltage of the WEH reached 5 V, ensuring a completely independent and stable power supply for wireless temperature and humidity sensors. Furthermore, the Mag-Boost mechanism also shows considerable potential for harvesting other forms of low-frequency environmental energy, including wind, vibration, and human kinetic energy. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Water waves

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Insight into the metal-free electrocatalysis of heteroatom-doped carbon nanocages in competitive CO_(2) reduction and H_(2) evolution

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Nano Research 2025年第2期18卷 255-262页

作者： Liu Jiao Chenghui Mao Biao Feng Fengfei Xu Shuo Li Jun Zhong Mingqi Xia Ruonan Cui Xizhang Wang Lijun Yang Qiang Wu Zheng Hu Key Laboratory of Mesoscopic Chemistry of Ministry of Education(MOE)and Jiangsu Provincial Laboratory for Nanotechnology School of Chemistry and Chemical EngineeringNanjing UniversityNanjing 210023China Institute of Functional Nano and Soft Materials Laboratory(FUNSOM) Jiangsu Key Laboratory of Advanced Negative Carbon TechnologiesSoochow UniversitySuzhou 215123China

Metal-free carbon-based catalysts exhibit diverse electrocatalytic performances in CO_(2) reduction reaction(CO_(2)RR),but the attributions and contributions of active sites are still confusing to ***,the hierarchical carbon nanocages(hCNC)doped with different heteroatoms(B,N,P,S)are prepared to examine the impact of dopants on the competitive CO_(2)RR and hydrogen evolution reaction(HER).The hCNC and P-doped hCNC show little CO_(2)RR activity,B-and S-doped hCNC show weak CO_(2)RR activity,while N-doped hCNC presents high CO_(2)RR *** CO Faradaic efficiency(FECO)of N-containing hCNC increases almost linearly with increasing the N content,even with the co-existing B or P.S and SN-doped hCNC more facilitate the HER.16 doping configurations are constructed,and up to 53 sites are examined CO_(2) H2O H2 CO*H*COOH*CO for the electrochemical activities with a constant potential modelling *** pyridinic-N(N^(*))is the best active site for CO_(2)RR to CO,while CBO_(2)H_(2)-1(αC^(*)),CBO_(2)H_(2)-2(γC^(*)),NO-1(βC^(*)),PO_(2)H-3(αC^(*))and SO_(3)H-3(δC^(*))are active for *** optimized FECO achieves 83.6%for N-doped hCNC with 9.54 at.%nitrogen,and S-doped hCNC reaches ca.30 mA·cm^(-2) current density for *** study unveils the structure-performance correlation of heteroatom-doped hCNC,which is conducive to the rational design of advanced metal-free carbon-based catalysts.

关键词： heteroatom-doping metal-free carbon-based catalyst CO_(2)reduction reaction hydrogen evolution reaction active sites density functional theory(DFT)

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Self-powered Smart Water Meter for Pipeline Flow Monitoring 5th

Self-powered Smart Water Meter for Pipeline Flow Monitoring

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5th International Conference on Vibration and Energy Harvesting Applications, VEH 2024

作者： Mei, Mingqi Wang, Zizhao Li, Yunfei Tang, Tianyi Liu, Huicong School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou215123 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

ISBN: (纸本)9789819611904

This paper presents the development of a water flow energy harvester (WFEH) designed to power smart water meters within residential piping systems. Utilizing the kinetic energy of flowing water, the device harnesses this energy and converts it into electricity via electromagnetic principles. The impeller's shape, quantity, and guide vane height at the inlet were simulated using computational fluid dynamics (CFD) software, comparing impeller rotational speeds under various simulation parameters to select the appropriate impeller configuration. Experimental data indicate that with an inlet water pressure of 0.12 MPa and an outlet pressure of 0.06 MPa, a single coil in the device generates a peak voltage of 3.6 V, with a peak power of 54.2 mW and a head loss of 6 m. The maximum head loss permitted in residential water pipes is 10 m. The energy generation module of the WFEH has been designed with a six-coil array, significantly enhancing its performance output. Theoretically, the output power of the WFEH is sufficient to meet the energy needs of smart water meters. Therefore, the WFEH holds potential for integration into urban water management systems, providing a stable energy supply for data collection and transmission, and contributing to the development of self-powered smart meters. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Water meters

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Versatile Wearable Interface: Multi-modal Tactile Sensing and Haptic Feedback for Human-Computer Interaction 5th

Versatile Wearable Interface: Multi-modal Tactile Sensing an...

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5th International Conference on Vibration and Energy Harvesting Applications, VEH 2024

作者： Liu, Ming Chen, Tao Zhu, Minglu Jiangsu Provincial Key Laboratory of Advanced Robotics School of Mechanical and Electric Engineering Soochow University Suzhou215123 China School of Future Science and Engineering Soochow University Suzhou215123 China

ISBN: (纸本)9789819611904

Human-machine interaction is an important aspect of exchanging information between the virtual reality world and the physical world. Haptic information interaction enables users to have immersive and efficient interactions, enhancing the overall experience. With the increasing number of tactile sensors and actuators in human-machine interaction systems, there are new requirements for multifunctional integration, lightweight energy consumption, and portability of devices. This paper proposes a human-machine interaction interface based on multi-modal haptic sensing and feedback fusion devices. This human-machine interaction interface has the following functions: touch-slip sensation sensing based on triboelectric-piezoelectric hybrid effect, vibration feedback, and pneumatic pressure feedback. We apply this human-machine interaction interface to robot digital twin interaction control, providing users with a more realistic control experience through the integration of haptic sensing and feedback. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Piezoelectric actuators

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High-precision trajectory calibration for robot surface machining by meta-heuristic search algorithm from local error in machining area

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International Journal of advanced Manufacturing Technology 2025年第5期138卷 2281-2295页

作者： Bi, Guangde Lu, Lei Zhang, Lei Fan, Cheng Sun, Lining School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou215123 China State Key Laboratory of Robotics and System Harbin Institute of Technology Harbin150001 China

Industrial robots used in complex surface machining are gradually increasing because of their high flexibility and large workspace. However, due to their low geometric accuracy, their application in high-precision surface machining is hindered. In order to improve the machining accuracy, a high-precision robot trajectory calibration method for complex surface machining is proposed. In the machining process, the machining trajectory is usually only within a certain machining area. In the method, high-precision standard balls are set at the boundary of the machining area. Based on the precise position information of these standard balls, the trajectory of the robot in the machining area is calibrated. Compared with the global robot calibration method, the proposed method can calibrate the robot trajectory in the machining area through standard parts, which will improve the trajectory accuracy of the machining robot to the level of repeated positioning accuracy. In this method, a large amount of standard ball data is obtained by the in-place measuring device before the machining process. Then, based on these data, a meta-heuristic search algorithm is used to calibrate the kinematic deviation of the robot measurement and the machining system. The trajectory of the machining robot is adjusted by the kinematic deviation after calibration, and it is applied to the motion control of the machining process to improve the positioning accuracy of the machining trajectory. In the end, to verify the accuracy and practicability of the machining trajectory obtained by this method, measurement and machining experiments are carried out. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Industrial robots

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Evetac Meets Sparse Probabilistic Spiking Neural Network: Enhancing Snap-Fit Recognition Efficiency and Performance

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IEEE robotics and Automation Letters 2025年第6期10卷 5353-5360页

作者： Fang, Senlin Ding, Haoran Liu, Yangjun Liu, Jiashu Zhang, Yupo Li, Yilin Kong, Hoiio Yi, Zhengkun Faculty of Data Science City University of Macau 999078 China Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Shenzhen518055 China City University of Hong Kong Hong Kong University of Macau 999078 China Shenzhen Institute of Advanced Technology Shenzhen518055 China City University of Macau 999078 China Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China City University of Macau Faculty of Data Science 999078 China Chinese Academy of Sciences Key Laboratory of Biomedical Imaging Science and System Shenzhen518055 China

Snap-fit peg-in-hole assembly is common in industrial robotics, particularly for 3 C electronics, where fast and accurate tactile recognition is crucial for protecting fragile components. Event-based optical sensors, such as Evetac, are well-suited for this task due to their high sparsity and sensitivity in detecting small, rapid force changes. However, existing research often converts event data into dense images and processing them with dense methods, leading to higher computational complexity. In this letter, we propose a Sparse Probabilistic Spiking Neural Network (SPSNN) that utilizes sparse convolutions to extract features from the event data, avoiding computations on non-zero cells. We introduce the Forward and Backward Propagation Through Probability (FBPTP) method, which enables simultaneous gradient computation across all time steps, eliminating the need for the step-by-step traversal required by traditional Forward and Backward Propagation Through Time (FBPTT). Additionally, the Temporal Weight Prediction (TWP) method dynamically allocates weights for different time outputs, enhancing recognition performance with minimal impact on model efficiency. We integrate the Evetac sensor compactly into our robotic system and collected two datasets, named Tactile Event Ethernet (TacEve-Eth) and Tactile Event Type-C (TacEve-TC), corresponding to cantilever and annular snap-fit structures. Experiments show that the SPSNN achieves the superior trade-off between recognition performance and efficiency compared to other widely used methods, achieving the highest average recognition performance while reducing inference time by over 90% compared to FBPTT-based dense SNN baselines. © 2016 IEEE.

关键词： Robotic assembly

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A Biomimetic Stress Field Modulation Strategy Inspired by Scorpion Compound Slit Sensilla Enabled High-Accuracy and Low-Power Positioning Sensor for Identifying the Load Incident Angles

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Journal of Bionic Engineering 2025年第2期22卷 727-738页

作者： Junqiu Zhang Yu Chen Haoran Li Jiqi Gao Xiangbo Gu Jiachao Wu Xiaojing Qin Kejun Wang Bin Zhu Daobing Chen Tao Sun Jianhua Fan Zhiwu Han Luquan Ren Key Laboratory of Bionic Engineering Ministry of EducationJilin UniversityChangchun130022People’s Republic of China Institute of Structured and Architected Materials Liaoning Academy of MaterialsShenyang110167China Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow UniversitySuzhou215123People’s Republic of China School of Mechanical Engineering Sciences University of SurreyGuildfordGU27XHUK The Institute of Technological Science Wuhan UniversityWuhan430072China School of Mechanical and Aerospace Engineering Jilin UniversityChangchun130025China

Numerous arthropods evolve and optimize sensory systems, enabling them to effectively adapt complex and competitive habitats. Typically, scorpions can precisely perceive the prey location with the lowest metabolic rate among invertebrates. This biological phenomenon contrasts sharply with engineered systems, which generally associates high accuracy with substantial energy consumption. Inspired by the Scorpion Compound Slit Sensilla (SCSS) with a stress field modulation strategy, a bionic positioning sensor with superior precision and minimal power consumption is developed for the first time, which utilizes the particular Minimum Positioning Units (MPUs) to efficiently locate vibration signals. The single MPU of the SCSS can recognize the direction of collinear loads by regulating the stress field distribution and further, the coupling action of three MPUs can realize all-angle vibration monitoring in plane. Experiments demonstrate that the bionic positioning sensor achieves 1.43 degrees of angle-error-free accuracy without additional energy supply. As a proof of concept, two bionic positioning sensors and machine learning algorithm are integrated to provide centimeter (cm)-accuracy target localization, ideally suited for the man-machine interaction. The novel design offers a new mechanism for the design of traditional positioning devices, improving precision and efficiency in both the meta-universe and real-world Internet-connected systems.

关键词： Scorpion Compound Slit Sensilla(SCSS) Vibration Source Stress Field Modulation Strategy High-accuracy and low-power Positioning Sensor Minimum Positioning Unit(MPU)

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Multi-particles micromanipulation in focused sound field for single-beam acoustic tweezer

Multi-particles micromanipulation in focused sound field for...

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2024 International Conference on Mechatronic Engineering and Artificial Intelligence, MEAI 2024

作者： Zhu, Guolong Li, Bo Xu, Junlei Zhang, Lue Sun, Lining School of Mechanical and Electric Engineering Jiangsu Provincial Key Laboratory of Advanced Robotics Soochow University Suzhou215137 China

ISBN: (纸本)9781510689176

In this paper, for the prediction of sound field distribution, a spherical piezoelectric transducer is designed for focused acoustic field according to piezoelectricity equation and vibration analysis. Based on the predicted acoustic fields distribution, the Acoustic Radiation Force(ARF) of the particles with different material and different sizes are obtained, according to a perturbation expansion of the acoustic fields in fluid. Besides, the trapping and micro-manipulation of particles are realized perpendicular to the acoustic axis through experimental according to the predicted ARF. © 2025 SPIE.

关键词： Acoustic fields

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Promoting effect of d-band center and in-situ precipitation strategy on sulfur evolution reaction

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Journal of Energy Chemistry 2025年第5期104卷 740-748页

作者： Jun Pu Yun Tan Jiasen Yin Zihan Shen Shoujie Liu Kai Zhang Lei Zhou Yagang Yao Key Laboratory of Functional Molecular Solids Ministry of EducationAnhui Provincial Engineering Laboratory for New-Energy Vehicle Battery Energy-Storage MaterialsCollege of Chemistry and Materials ScienceAnhui Normal UniversityWuhu 241002AnhuiChina National Laboratory of Solid State Microstructures College of Engineering and Applied SciencesJiangsu Key Laboratory of Artificial Functional MaterialsCollaborative Innovation Center of Advanced MicrostructuresNanjing UniversityNanjing 210093JiangsuChina State Key Laboratory of Multiphase Complex Systems Institute of Process EngineeringChinese Academy of SciencesBeijing 100190China School of Materials Science and Engineering Anhui UniversityHefei 230601AnhuiChina School of Energy and Power Engineering Jiangsu UniversityZhenjiang 212013JiangsuChina Department of Electrical Engineering Eindhoven University of TechnologyEindhoven MB 5600the Netherlands

The slow conversion of polyphase in lithium-sulfur(Li-S)batteries not only intensifies the shuttle effect of lithium polysulfides(LiPSs),but also causes the continuous accumulation of inactive sulfur species,resulting in rapid capacity attenuation and sluggish dynamic ***,the promoting effect of atomic interface stress on sulfur reaction was investigated via CoFe-CoFe_(2)O_(4)heterogeneous nanosheets with a cavity *** strain force induced by the in-situ precipitation of Co Fe bimetallic alloy in oxide matrix increased the d-band center,which was conducive to the interaction between catalyst and Li *** sulfur cathode based on this two-dimensional(2D)nanosheet design showed an extremely high capacity of 751 mAh g^(-1)at 4 *** with a sulfur loading of 5.55 mg cm^(-2),its area capacity was still as high as 7.15 mAh cm^(-2).Meanwhile,the enhanced stability greatly improved the practical potential of Li-S batteries.

关键词： 2D nanosheets In-situ precipitation Strain effect D-band center Li-S batteries

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Hierarchical Loss Constraint Filter for Low-Visibility Tiny Crack Detection

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IEEE Transactions on Automation Science and Engineering 2025年 22卷 15188-15199页

作者： Zheng, Lijing Yi, Zhengkun Xu, Tiantian Wang, Can Shang, Wanfeng University of Chinese Academy of Sciences China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Beijing100045 China Chinese Academy of Sciences Guangdong Provincial Key Laboratory of Robotics and Intelligent System Shenzhen Institute of Advanced Technology Shenzhen518055 China Shenzhen University Shenzhen518060 China Chinese Academy of Sciences Shenzhen Institute of Advanced Technology Shenzhen518055 China

Tiny crack detection plays a vital role in ensuring the safety of critical industrial components and infrastructure, providing early intervention to prevent crack propagation and mitigate potential damage. Although machine vision-based defect detection has been greatly utilized in safety inspection and maintenance, tiny crack detection remains a big issue due to the low visibility and weak features with large background noise. To address this challenge, this paper presents an end-to-end trainable hierarchical constrained neural network for tiny crack detection. Firstly, we present a hierarchical loss constraint Filter (HLCF) module based on a region-level attention mechanism to capture the holistic vein structure of cracks and enhance the faint feature extraction of tiny cracks. In order to balance local high contrast and holistic crack features, a feature fusion loss constraint is designed to reduce noise interference and refine the boundary details of cracks by learning different receptive fields in each layer. Besides that, we created a dataset consisting of tiny crack samples collected through image processing and manual labeling from industrial production, named TinyCrack. The proposed HLCF model is evaluated on TinyCrack and seven public databases. The experimental indices show that the model achieves Precision over 0.62%, Recall over 0.12%, F-score over 0.53%, and IOU over 1.12% in TinyCrack, demonstrating the best accuracy compared with other public crack detection models. The results indicate that the HLCF detection model has better performance in identifying low-visibility tiny cracks. © 2004-2012 IEEE.

关键词： Leak detection

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