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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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Efficient gene disruption in polyploid genome by Cas9–Trex2 fusion protein

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Journal of Integrative Plant Biology 2025年第1期67卷 7-10页

作者： Wenbo Pan Chunlei Gao De Niu Jinghua Cheng Jiao Zhang Xiying Yan Qiang Long Yao Yao Zhu Wenjing Sun Qi Xie Yuehui He Xing Wang Deng Huawei Zhang Jian Li State Key Laboratory of Wheat Improvement Shandong Laboratory of Advanced Agricultural Sciences in WeifangPeking University Institute of Advanced Agricultural SciencesWeifang 261325China Guangdong Provincial Key Laboratory of Biotechnology for Plant Development Ministry of Education&Guangdong Provincial Key Laboratory of Laser Life ScienceSchool of Life SciencesSouth China Normal UniversityGuangzhou 510631China Shenzhen Tidy field System Biotechnology Co. LtdShenzhen 518107China State Key Laboratory of Plant Genomics Institute of Genetics and Developmental BiologyThe Innovative Academy of Seed DesignChinese Academy of SciencesBeijing 100101China

Despite significant advancements in the efficiency of the CRISPR/Cas9 genome editing system over the past decade,performing gene disruption in polyploid crops such as common wheat(Triticum aestivum L.)remains challeng... 详细信息

关键词： CRISPR/Cas9 crops trait

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Degradation state prediction of IGBT based on optimized variational mode decomposition and hybrid models

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Electrical Engineering 2025年 1-21页

作者： He, Deqiang Wang, Yanbo Wu, Jinxin Jin, Zhenzhen Zhao, Ming Chen, Yanjun State Key Laboratory of Featured Metal Materials and Life-Cycle Safety for Composite Structures Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical Engineering Guangxi University Nanning530004 China Guangxi Liugong Machinery Co. Ltd Liuzhou545007 China State Key Laboratory of Heavy-Duty and Express High-Power Electric Locomotive Zhuzhou412001 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology Guilin541004 China

The insulated gate bipolar transistor (IGBT) is critical in motor drivers. Prolonged operation can lead to gradual degradation, adversely affecting motor operation and system performance. Therefore, accurate IGBT degradation state prediction (DSP) is essential for the timely maintenance and stable operation of motor drivers. The existing methods make it difficult to effectively deal with complex original degradation data, resulting in low DSP accuracy. To solve this issue, an improved method called ISSA-VMD-FDE based on optimized variational mode decomposition (VMD) and hybrid models is presented. Initially, the improved sparrow search algorithm (ISSA) is employed to optimize the VMD with minimum envelope entropy to obtain the key parameter combinations affecting VMD performance. Subsequently, the optimized VMD decomposes the IGBT degradation state data into multiple intrinsic mode functions (IMFs) and calculates each IMF’s fluctuation-based dispersion entropy (FDE). Based on the magnitude of FDE, the IMFs are categorized into high-frequency and low-frequency components. Finally, different models are applied to predict the high-frequency and low-frequency components separately, and the predictions are combined to yield the final DSP results. Experimental results demonstrate that the proposed prediction method exhibits strong predictive performance and reduces the MAPE by 89.41% compared to conventional methods, effectively supporting predictive maintenance and ensuring the reliable operation of IGBT. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2025.

关键词： Insulated gate bipolar transistors (IGBT)

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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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Edge-Cloud Cooperation-Driven Intelligent Sustainability Evaluation Strategy Based on IoT and CPS for Energy-Intensive Manufacturing Industries

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IEEE Internet of Things Journal 2025年第9期12卷 12287-12297页

作者： Ma, Shuaiyin Huang, Yuming Chen, Yanping Xiao, Qinge Xu, Jun Leng, Jiewu Xi’an University of Posts and Telecommunications Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi’an Key Laboratory of Big Data and Intelligent Computing School of Computer Science and Technology Xi’an710121 China Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China Xidian University Advanced Manufacturing Technology Innovation Center Guangzhou Institute of Technology Guangzhou510555 China Guangdong University of Technology Guangdong Provincial Key Laboratory of Computer Integrated Manufacturing System State Key Laboratory of Precision Electronic Manufacturing Technology and Equipment Guangzhou510006 China

The advancement of the Industry 5.0 in information technology has led to increased interest in integrating edge-cloud cooperation with Internet of Things (IoT) and cyber-physical system (CPS) designs. This integration effectively reduces service delays and provides real-time analysis feedback to physical spaces, attracting attention from both academia and industry. These advanced technologies enhance production system intelligence, their alignment with circular economy principles for promoting sustainability has been overlooked. To address this gap, this article proposes an intelligent sustainability evaluation strategy driven by edge-cloud cooperation, IoT, and CPS. The proposed approach aims to enhance production sustainability and intelligence through circular economy perspectives. It introduces improved gray relation analysis and deep clustering network techniques to extract meaningful insights from diverse indicators within the evaluation system. By analyzing relationships between different equipment and workshops, it provides an analytical method that enhances production efficiency while reducing energy consumption and resource waste. To further validate the proposed method, an illustrative example using a partner company’s production data demonstrates its accuracy. © 2014 IEEE.

关键词： Circular economy

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Multi-view fully connected graph to fuse multi-sensor signals for mechanical equipment remaining useful life prediction

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Journal of Manufacturing systems 2025年 80卷 1029-1052页

作者： Wu, Jinxin He, Deqiang Jin, Zhenzhen Zhao, Ming Li, Xianwang Chen, Yanjun Guangxi Key Laboratory of Manufacturing System & Advanced Manufacturing Technology School of Mechanical Engineering Guangxi University Nanning530004 China State Key Laboratory of Heavy-duty and Express High-power Electric Locomotive Zhuzhou412001 China Guangxi Key Laboratory of Precision Navigation Technology and Application Guilin University of Electronic Technology 541004 China Guangxi Liugong Machinery Co. Ltd Liuzhou545007 China

Accurate remaining useful life prediction is essential for enhancing equipment reliability and optimizing maintenance strategies. However, existing methods struggle to effectively integrate multi-sensor data while quantifying uncertainty. To address these challenges, a multi-view fully connected graph neural network is proposed for multi-sensor mechanical equipment remaining useful life prediction. Firstly, local fully connected graphs and global graphs are constructed to comprehensively characterize the multi-view spatial correlations from global and local views. Meanwhile, the graph convolution operations are performed on local and global graphs to extract the intricate spatial dependencies within multi-sensor signals. Then, the learned multi-view spatial representations are fed into the temporal convolutional network to capture the temporal dependencies across sensor timestamps. Finally, a joint optimization network is developed to simultaneously predict the remaining useful life and its associated prediction interval, enabling uncertainty quantification. Extensive experiments on two multi-sensor monitoring degradation datasets demonstrate the superior performance of the proposed model, offering valuable technical support for predictive maintenance. © 2025 The Society of Manufacturing Engineers

关键词： Predictive maintenance

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Deep Learning and Projection Neural Network With Finite-Time Convergence for Energy Management of Multi-Energy system

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IEEE Transactions on Smart Grid 2025年第3期16卷 2156-2168页

作者： Liu, Xueying He, Xing Li, Chaojie Huang, Tingwen Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System With Renewable Energy Sources Beijing102206 China Shenzhen University of Advanced Technology Faculty of Computer Science and Control Engineering Shenzhen518055 China

In this paper, an approach based on projection neural network (PNN), sliding mode control technique, and deep learning is proposed to solve the energy management problem of multi-energy systems (MES) containing dynamic parameters. First, the sliding mode technique is introduced in the PNN to design a finite-time PNN (FTPNN). The stability and finite-time convergence of the proposed FTPNN are proved by the Lyapunov method and the setting time bound is given. Then, the deep FTPNN (DFTPNN) is designed by combining deep learning with the proposed FTPNN. The dynamic parameters in the MES that change over time are used as input variables for the DFTPNN, allowing the trained DFTPNN to respond immediately to changes in the dynamic parameters and to predict the solutions of the FTPNN with different parameters directly. Simulation experiments show that FTPNN has faster convergence compared to PNN. DFTPNN significantly reduces the computation time compared to FTPNN. DFTPNN provides predicted solutions to FTPNN. Since DFTPNN can respond immediately to changes in dynamic parameters and directly provide energy management strategies under different parameters, it can adapt to changing environments and promote the economic and stable operation of MES. © 2010-2012 IEEE.

关键词： Cogeneration Heuristic algorithms Convergence Deep learning Prediction algorithms Optimization Heating systems Energy management Approximation algorithms Power system stability

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Bulk photovoltaic effect in two-dimensional ferroelectric α-In2Se3

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Science China(Information Sciences) 2025年第2期68卷 285-292页

作者： Huiting WANG Shuaiqin WU Yan CHEN Qianru ZHAO Jinhua ZENG Ruotong YIN Yuqing ZHENG Chang LIU Shukui ZHANG Tie LIN Hong SHEN Xiangjian MENG Jun GE Xudong WANG Junhao CHU Jianlu WANG State Key Laboratory of Infrared Physics Shanghai Institute of Technical PhysicsChinese Academy of Sciences University of Chinese Academy of Sciences Shanghai Frontiers Science Research Base of Intelligent Optoelectronics and Perception Institute of OptoelectronicsFudan University Hangzhou Institute for Advanced Study University of Chinese Academy of Sciences Frontier Institute of Chip and System Fudan University

Non-centrosymmetric material systems can generate spontaneous photocurrent without a p-n junction under uniform illumination, which is known as the bulk photovoltaic effect（BPVE）. It has garnered significant attention for its application potential in the fields of energy harvesting and photoelectric detection. In this study, we report the BPVE observed in twodimensional ferroelectric α-In2Se3multilayer flakes. The reversal in spontaneous photocurrent polarity is observed and associated with the out-of-plane ferroelectric polarization direction. The spontaneous photocurrent originating from the shift current is also polarization-sensitive. The amplitude of spontaneous photocurrent is further increased by one order of magnitude through vacuum annealing. Our results provide a further application potential for new-generation photodetectors based on BPVE with low power consumption and multi-dimensional optoelectronic detection.

关键词： bulk photovoltaic effect 2D ferroelectrics 3R $\alpha$-In$_2$Se$_3$ shift current self-powered photodetection

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Performance assessment of octagonal layout based integrated photonic reservoir computing for optical channel equalization

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Optics Express 2025年第7期33卷 15814-15826页

作者： Zuo, Xiaoyan Pei, Li Bai, Bing Bai, Bowen Hu, Zhouyi Wang, Jianshuai Qu, Qi Dong, Fei Institute of Lightwave Technology Beijing Jiaotong University Ministry of Education Key Laboratory of All-Optical Networks and Modern Communication Networks Beijing100044 China Technology Co. Ltd. China State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Beijing Aerospace Institute for Metrology and Measurement Technology Beijing100076 China

Compared to electronic equalizers, the equalizers based on integrated photonic reservoir computing exhibit the advantages of large bandwidth, short latency, and low power consumption. The existing rectangular reservoir layout employs internal nodes as input and output nodes, leading to inadequate signal exchange and extra noise due to waveguide crossing. We proposed an optical channel equalizer based on an octagonal reservoir layout and verified its equalization ability. The system-level simulation of the proposed layout is conducted to investigate the impact of layout shape, node quantity, input and output port number, and weight algorithm on channel equalization performance in a 25Gbaud 4-level pulse amplitude modulation optical communication system. Results indicate that, within a transmission distance of 5-30 km, the bit error rate (BER) of the equalized signal in the proposed octagonal layout is lower than that of a rectangular layout with the same number of nodes. Specifically, at 5 km, the octagonal layout’s BER is 5 and 10 orders of magnitude lower than the rectangular layout and the feedforward equalizer, respectively. Meanwhile, the best readout algorithm, K-nearest neighbor regression, achieves an equalized BER of 7.012 × 10−14 © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Pulse amplitude modulation

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Geometry-Based Stochastic MIMO Channel Model for Near-Field and Far-Field Scenarios of Integrated Sensing and Communications

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IEEE Transactions on Vehicular Technology 2025年第5期74卷 6928-6940页

作者： Jin, Yunwei He, Ruisi Ai, Bo Yuan, Yuan Niu, Yong Zhang, Haoxiang Beijing Jiaotong University State Key Laboratory of Advanced Rail Autonomous Operation School of Electronics and Information Engineering Frontiers Science Center for Smart High-speed Railway System Beijing100044 China Ministry of Industry and Information Technology China Academy of Industrial Internet Beijing100804 China

Integrated sensing and communication (ISAC) has become a promising technology in current sixth-generation (6G) wireless communications due to its ability to support both communication and sensing. Existing works regarding ISAC channels mainly focus on far-field propagation conditions based on plane wave assumption. However, with the deployment of ultra-massive multiple-input multiple-output (MIMO), the far-field assumption may no longer hold, as the communication and sensing propagation distances distances may be comparable to the array aperture size. To address this issue, we propose a three-dimensional MIMO channel model that simultaneously considers near-field and far-field propagation conditions in ISAC environments. Additionally, we distinguish the communication and sensing propagation environments by employing different effective scatterer distributions. In the model, the sensing channel is divided into target sensing and environmental sensing components, whereas the communication channel is divided into line-of-sight and non-line-of-sight components. The weighted sum of each component in communication and sensing channels results in accurate channel representations. Based on the proposed model, we derive and thoroughly investigate space-time-frequency correlation function, normalized absolute error function, and channel capacity. A key observation is that the channel correlation strongly depends on scatterer distribution. In certain conditions, the sensing channel can effectively capture the behavior of the communication channel. Error function results indicate that the proposed channel model achieves fairly high accuracy in ultra-massive MIMO scenarios. These findings provide essential support for the development of ISAC systems in future 6G wireless communications. © 1967-2012 IEEE.

关键词： Stochastic systems

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