检索结果-内蒙古大学图书馆

2024 9th International Seminar on Computer technology, Mechanical and Electrical Engineering, ISCME 2024

作者： Li, Fengyu Liu, Xiaoming Liu, Zhilong Yu, Yang State Key Laboratory of High-end Compressor and System Technology Hefei General Machinery Research Institute Co. Ltd Hefei230031 China

—As one of the main components of hydrogen refueling stations, the diaphragm hydrogen compressor is an important equipment supporting the development of hydrogen energy in China. Currently, the diaphragm hydrogen compressor is facing a significant demand for improving reliability. The diaphragm is one of the vulnerable parts in diaphragm hydrogen compressors, mainly affected by the difference between hydraulic oil pressure and hydrogen pressure, which deforms and compresses hydrogen gas to achieve the operation of diaphragm hydrogen compressors. This study selected the oil gas pressure difference as the independent variable and conducted simulation research on the variation laws of equivalent stress and axial deformation of the diaphragm. © 2025 Institute of Physics Publishing. All rights reserved.

关键词： Diaphragms

来源：评论

学校读者我要写书评

暂无评论

Intensified Tropical Cyclones in Global Climate Simulations Employing a High-Wind Drag Relation over Sea Surface

引用

Journal of Climate 2025年第10期38卷 2205-2223页

作者： Wei, Wei Lu, Yixiong Yang, Shunan Liu, Yiming Wu, Tongwen Song, Zhenya Qiao, Fangli Li, Yubin Wu, Fanghua Rong, Xinyao Li, Jianglong CMA Earth System Modeling and Prediction Centre China Meteorological Administration Beijing China State Key Laboratory of Severe Weather Meteorological Science and Technology CMA Earth System Modeling and Prediction Centre Beijing China National Meteorological Centre China Meteorological Administration Beijing China First Institute of Oceanography Key Laboratory of Marine Science and Numerical Modeling Ministry of Natural Resources Qingdao China School of Atmospheric Physics Nanjing University of Information Science and Technology Nanjing China

Accurate parameterization of momentum fluxes at high winds across the air–sea interface is crucial for realistically simulating the surface wind fields of tropical cyclones (TCs). Climate models often struggle to reproduce observed TC intensities, particularly surface wind speeds. This study evaluates the performance of a revised surface drag scheme with a capping drag coefficient at high wind speeds in global climate models (GCMs). Our results indicate that the revised surface drag scheme significantly improves the representation of TCs in a statistical sense: (i) Not only the mature intensity but also the maximum 10-m wind speeds throughout the TC life cycle are largely enhanced;(ii) biases in the pressure–wind relationship are notably alleviated, exhibiting reasonable agreement with observations;and (iii) other TC statistical features, such as TC number, central sea level pressure, individual lifetime, and intensification rate, show relatively minor sensitivity to the surface drag schemes. The main reason for the superiority of the revised high-wind drag relation is that the lower drag coefficient in high-wind conditions significantly increases the maximum potential intensity (MPI), indicating a higher theoretical limit for TC intensities. These findings are consistent across different model versions and resolutions, highlighting the robustness of the high-wind surface drag parameterization in controlling the simulated surface wind speeds of TCs in GCMs. © 2025 American Meteorological Society.

关键词： Tropical cyclone

来源：评论

学校读者我要写书评

暂无评论

Data-Efficient Synchronous Machine Winding Short Circuit Faults Diagnosis Based on Frequency Response Analysis and Active Learning

引用

IEEE Sensors Journal 2025年第11期25卷 19804-19815页

作者： Chen, Yu Zhao, Zhongyong Liu, Jiangnan Wang, Wei Yao, Chenguo Huazhong University of Science and Technology State Key Laboratory of Advanced Electromagnetic Technology School of Electrical and Electronic Engineering Wuhan430074 China Southwest University College of Engineering and Technology Chongqing400716 China Yibin Academy of Southwest University Sichuang64400 China Guangzhou Power Supply Bureau of Guangdong Power Grid Co. Ltd Guangzhou510620 China Chongqing University State Key Laboratory of Power Transmission Equipment and System Security and New Technology Chongqing400044 China

Winding short-circuit (SC) faults are a prevalent issue in synchronous machines, and the accurate and timely identification of these faults is critical for maintaining power system stability. Current methods for inspecting synchronous machine windings often rely on periodic inspections based on human expertise. Numerous studies have explored the application of deep learning (DL) models for detecting synchronous machine winding SC faults. However, these models often exhibit excessive complexity and overlook physical overhead, leading to inefficient utilization of computational power and data resources. To address these limitations, this study proposes a dual-channel deep learning (DL) model integrated with the active learning (AL) query strategy. In this study, winding SC faults are manually simulated on a 5 kVA synchronous machine, and corresponding frequency response analysis (FRA) data are recorded. Subsequently, the proposed method is validated on the test set and benchmarked against previous studies. Experimental results demonstrate that the proposed method significantly reduces the data annotation effort and accelerates model training to the order of seconds (under 5 seconds) while maintaining satisfactory accuracy (≥ 95%). Comparative experimental results further indicate that the proposed model, requiring only 1/20th of the labeled training samples used in previous studies, achieves a substantial reduction of approximately 99.8% in both model parameters and training time. © 2001-2012 IEEE.

关键词： Synchronous machinery

来源：评论

学校读者我要写书评

暂无评论

Enhancing Ultrasound Scanning Skills in a Leader-Follower Robotic system through Expert Hand Impedance Regulation

引用

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Niu, Baoshan Yang, Dapeng Zhang, Le Ji, Yiming Jiang, Li Liu, Hong Harbin Institute of Technology State Key Laboratory of Robotics and Systems Harbin150080 China

Traditional breast cancer surgeries require collaboration between ultrasound (US) doctors and surgeons, making the procedure complex and treating physicians prone to fatigue. In leader-follower robotic surgery, a surgeon controls an US robotic arm and an instrument robotic arm with their left and right hands, enabling independent surgical performance. However, the lack of US scanning skills among surgeons, as well as the physical separation in leader-follower operations, can negatively impact both the scanning and surgical outcomes. This paper proposes a robot-assisted scheme based on dynamic arm impedance compensation (IC) that references expert arm stiffness to compensate for novice arm stiffness. The impedance compensator adjusts the compensation strategy according to the scanning area and scanning stage. The impedance force generator estimates the scanning direction via Kalman filtering and applies stiffness and damping forces in the vertical direction to suppress tremors and other involuntary movements. The experimental results revealed that during the coarse and fine scanning phases, the probe position variance decreased by 57.9% and 73.6%, the contact force variance decreased by 55.2% and 42.5%, and the US image confidence increased by 22.0% and 23.8%, respectively. Compared with traditional filtering compensation (FC) schemes, this approach reduces the average position variance and contact force variance by 32.0% and 25.3%, respectively, and increases confidence by 7.3%. In a no-compensation test, the IC training group outperformed the FC group. This scheme can assist leader-follower US scanning and rapidly improve surgical skills. © 2013 IEEE.

关键词： Robotic surgery

来源：评论

学校读者我要写书评

暂无评论

On-orbit assembly control based on model predictive control and load online identification

引用

Advances in Space Research 2025年

作者： Li, Yuntao Xu, Zichun Zhao, Zhiyuan Zhuang, Lei Yang, Xiaohang Ma, Boyu Zhao, Jingdong Liu, Hong State Key Laboratory of Robotics and Systems Harbin Institute of Technology Harbin150001 China

Space manipulator are essential for on-orbit assembly (OOA) tasks. This paper proposes an advanced control framework to address key challenges in Cartesian trajectory tracking and compliant assembly. First, a rigid-body dynamic model of the manipulator is established using CAD model and joint friction identification. A Linear Extended state Observer (LESO) is designed to mitigate system errors by modeling both parametric uncertainties and external disturbances as an extended state. Additionally, the method for selecting parameters of the LESO, along with its convergence proof, is presented. Second, a model predictive control (MPC) algorithm is employed to optimize the Cartesian trajectory of the manipulator in real time. By linearizing the Cartesian space trajectory, the MPC problem is formulated as a QP problem. Solving this QP problem in real-time allows for the optimization of the Cartesian space trajectory, thereby improving the trajectory tracking accuracy in Cartesian space. Finally, online identification of load parameters, sensor zero drift, and temperature drift enables effective compliant assembly control. Extensive experiments validate the proposed methods: (1) The Cartesian trajectory tracking error is maintained below 0.1 mm. (2) After sensor compensation for identified load parameters, zero drift, and temperature drift, the maximum force residual is 0.42 N, and the maximum torque residual is 0.1 N m. © 2025 COSPAR

关键词： Predictive control systems

来源：评论

学校读者我要写书评

暂无评论

MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotating Machine based on Multi-scale Convolutional Extraction and Adaptive Pruning Fuzzy Inference system

MSC-APFNN: Fault Diagnosis Deep Transfer Network for Rotatin...

引用

International Conference on Artificial Intelligence and Autonomous Transportation, AIAT 2024

作者： Zhou, Yanrun Lei, Zihao Wen, Guangrui Su, Yu Zhang, Zhifen National Key Lab of Aerospace Power System and Plasma Technology Xi’an Jiaotong University Xi’an710049 China State Key Laboratory for Manufacturing Systems Engineering Xi’an Jiaotong University Xi’an710049 China Key Laboratory of Education Ministry for Modern Design and Rotor-Bearing System Xi’an710049 China

ISBN: (纸本)9789819639687

Intelligent fault diagnosis on mechanical transmission system of the train is of great importance to ensure the efficient operation of high-speed trains. However, due to the complex and changeable operation conditions (OCs), it is difficult to ensure the uniformity of the data distribution obtained by real-time acquisition, which sets a higher standard for the design of intelligent algorithms. Empowered by deep transfer learning (DTL) methods, the fuzzy inference system provides an effective solution to the abovementioned problem. In this paper, an Adaptive Pruning Fuzzy Inference system (APFIS) is proposed predicated on the fuzzy theory and the basic structure of fuzzy neural network, along with the hybrid model MSC-APFNN constructed by combining the multi-scale convolutional network with the proposed APFIS. This model can be employed as a backbone for the application to transfer diagnostic detection of rotating systems based on adversarial networks. Experimental results demonstrate that the proposed model achieves excellent diagnostic results under variable operating conditions, which provide a novel insight into the optimization of the traditional convolutional and fully-connected deep network structure and offer a promising avenue for the research of fuzzy decision-making in the context of fault diagnostics for high-speed trains. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： Fuzzy neural networks

来源：评论

学校读者我要写书评

暂无评论

MemoFlow: Modifying Explicit Motion of Inconsistency in Optical Flow 27th

MemoFlow: Modifying Explicit Motion of Inconsistency in Op...

引用

27th International Conference on Pattern Recognition, ICPR 2024

作者： Wang, Mengfei Shi, Wenjun Zhu, Dongchen Wang, Lei Li, Jiamao Bionic Vision System Laboratory State Key Laboratory of Transducer Technology Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences Shanghai200050 China University of Chinese Academy of Sciences Beijing100049 China

ISBN: (纸本)9783031781124

Due to the limitation of training datasets and global motion understanding to optical flow estimation, current methods only focus on local clues and ignore the motion continuity of consecutive frames, resulting in an inconsistent motion problem. After theoretical analysis, we find the multi-frame methods need to pay more attention to the continuity between cost volumes than two-frame methods. Thus, our method is based on a multi-frame framework and introduces extra object coordinates in each frame by the segmentation model to revise the matching pairs in cost volume. Specifically, we introduce a Cost Volume Adaptation Module, including a Bbox Spatial Queries to store coordinates information and a Correlation Query Queue to query the object position of different frames. On the Sintel and KITTI test benchmark, our proposed MemoFlow achieves 1.00 and 1.69 average endpoint error (AEPE) on the clean and final passes and an F1-all error of 4.43%, ranking 1st among all three-frame methods and two-frame methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Optical correlation

来源：评论

学校读者我要写书评

暂无评论

Characterization Method of Thermal Stability Boundary for High Proportion New Energy Power system Based on Sensitivity Clustering 9th

Characterization Method of Thermal Stability Boundary for Hi...

引用

9th Purple Mountain Form on Smart Grid Protection and Control, PMF 2024

作者： Wang, Weitao Xu, Taishan Zhang, Jinlong Bao, Yanhong Song, Jiaqi Yu, Mengjie Zhao, Xijie State Key Laboratory of Technology and Equipment for Defense Against Power System Operational Risks Nanjing211106 China NARI Technology Co. Ltd. Nanjing211106 China

ISBN: (纸本)9789819616701

Characterizing thermal stability boundary is fundamental to the operation and control of power system, directly impacting its safety and economic efficiency. With the integration of large-scale renewable energy sources, operation modes of power system have become increasingly complex and variable. The influence characteristics of multi-dimensional variables on system boundary tend toward strong nonlinearity and coupling, posing challenges for traditional characterization methods. This paper first analytically derives the thermal stability boundary manifold and its gradient vector trajectory based on a single-machine equivalent system, exploring the nonlinear nature of system boundary manifold. Secondly, considering the active-reactive power coupling characteristics of renewable energy devices, it analyzes the nonlinear and non-convex features and patterns of system boundary manifold under typical scenarios such as weak sending and weak receiving conditions. Finally, a segmented approximation method suitable for high-dimensional nonlinear boundary manifold is proposed. This method utilizes sensitivity analysis and the K-Medoids algorithm to perform clustering analysis of high-dimensional gradient vectors and segmentation of boundary manifold, followed by fitting linear approximation expressions for each segment using the least squares method. The practicality and effectiveness of the proposed method are validated through a case study of the IEEE 3-machine 9-bus system. © state Grid Electric Power 2025.

关键词： Sensitivity analysis

来源：评论

学校读者我要写书评

暂无评论

Research on an automatic light field refocusing method for a camera array

引用

Optics Express 2025年第10期33卷 20964-20980页

作者： Pan, Yihua Zhao, Yushan Li, Yuyang Liu, Bin Chen, Ping Shanxi Key Laboratory of Intelligent Detection Technology & Equipment School of Information and Communication Engineering North University of China Shanxi Taiyuan030051 China Key Laboratory of Photoelectronic Imaging Technology System of Ministry of Education of China School of Optics and Photonics Beijing Institute of Technology Beijing100081 China State Key Laboratory of Extreme Environment Optoelectronic Dynamic Testing Technology and Instruments North University of China Shanxi Taiyuan030051 China

Light field information includes the position and angle information of light in space. A camera array is one of the methods for obtaining light field information, which combines and arranges multiple cameras in space to achieve multi-angle imaging of the same target. Based on the light field information, a computational imaging method can be used to achieve refocusing technology for a camera array (also known as optical synthetic aperture imaging technology). In this technology, by setting the focusing coefficient, the light field information can be focused on different depths. This mode is called taking photos first and focusing later. However, in order to focus on a specific object surface, traditional refocusing methods require users to manually adjust the focusing coefficient multiple times, which is time-consuming and user-unfriendly. Therefore, in this paper, we propose an automatic focusing method based on the optimal focusing coefficient map (OFCM) to address the manual refocusing problem. This method transforms the refocusing problem into obtaining the optimal focus coefficient map. In the process of solving OFCM, the threshold filtering method and the neighborhood filling method are used to optimize the results and improve the robustness of the proposed method. The experimental results show that our proposed method has higher imaging quality and faster running efficiency compared to automatic refocusing methods based on disparity maps and gradient functions. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： Computational Imaging

来源：评论

学校读者我要写书评

暂无评论

A Novel Transfer Learning Method for Sub/Super-Synchronous Oscillation Mode Identification in Power systems

引用

IEEE Internet of Things Journal 2025年第11期12卷 18385-18396页

作者： Fang, Jiashu Kong, Lingran Li, Aobing Hu, Weihao University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu610054 China North China Electric Power University State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources Beijing102206 China

With the increasing integration of renewable energy in power systems, accurate and rapid mode identification of the sub/super-synchronous oscillation (sub/super-SO) is essential for ensuring grid security. This paper presents a deep learning (DL)-based method for early prediction of the dominant frequency in sub/super-SO events. To address the challenge of limited sub/super-SO samples for DL training, we propose a domain-adversarial neural network-based transfer learning framework, which leverages easily obtainable forced oscillation data to capture sub/super-SO features. Our method introduces several enhancements over previous approaches in both model structure and optimization. First, an encoder-decoder module with a mask component is designed to reconstruct missing data from field sub/super-SO events, utilizing a specialized reconstruction loss function. Second, the proposed dual-predictor configuration applies supervised learning in both source and target domains, imposing stronger optimization constraints. Furthermore, the proposed method exhibits generality and robustness when the topologies of target and source wind farms differ, highlighting its potential for practical applications in power systems. © 2014 IEEE.

关键词： Structural optimization

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：