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A Review of Machine Tool Motorized Spindle Variable Pressure Preload technology Research

Recent Patents on engineering 2025年第5期19卷

作者： Dai, Ye Deng, Hao Pan, Gaofeng Chen, Xinda Key Laboratory of Advanced Manufacturing and Intelligent Technology Ministry of Education School of Mechanical and Power Engineering Harbin University of Science and Technology Harbin150080 China School of College of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin150080 China

High-precision spindles are a vital study topic because of the growing demand for high-speed, high-precision, reliable, and long-lasting machine tools. Bearing preload is the primary means of improving the accuracy of the spindle rotor system. This patent study aims to summarize the research results, systematically analyze and evaluate the machine tool motorized spindle variable pressure preload technology, and provide a scientific basis for the practical application of the motor spindle variable pressure preload technology. This study is an in-depth analysis of the basic principles of the electric spindle preloading technology, the study of its influence mechanism on the thermal deformation, stiffness, slewing error, and life of the spindle system, and a discussion of the current shortcomings in the field of spindle preloading to provide a reference for the subsequent optimization. In the spindle system, the performance of rolling bearings and the design of bearing preload have an important impact on the spindle's accuracy and stability. The traditional preload method has yet to meet the requirements of modern high-speed machine tools. Real-time controllable preload has become the new direction of development, but the current preload technology still has certain limitations and needs to be further improved. In the future, the spindle bearing variable pressure preload technology will be more efficient and intelligent. © 2025 Bentham Science Publishers.

关键词： Machine tools

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Optical attenuation coefficient decorrelation-based optical coherence tomography angiography for microvascular evaluation of Alzheimer's disease on mice

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Neurophotonics 2025年第1期12卷 015013页

作者： Xiang, Ben Ding, Ning Jiang, Huiwen Liu, Jian Yu, Yao Luan, Jingmin Zhao, Yuqian Wang, Yi Yang, Yanqiu Ji, Cheng Wang, Fengwen Ma, Zhenhe Northeastern University College of Information Science and Engineering Shenyang China Northeastern University at Qinhuangdao School of Control Engineering Qinhuangdao China Hebei Key Laboratory of Micro-Nano Precision Optical Sensing and Measurement Technology Qinhuangdao China Qinhuangdao Haigang Hospital Qinhuangdao China

Significance: The deep cortical microvasculature is closely linked to the pathogenesis of Alzheimer's disease (AD). However, tail artifacts from superficial cortical vessels often interfere with detecting deep vessels in optical coherence tomography angiography (OCTA) imaging. A more accurate method to assess deep cortical vasculature is crucial for understanding its relationship with AD onset. Aim: We aim to reduce superficial vessel artifacts in OCTA imaging and improve the visualization and analysis of deep cortical microvasculature in an AD mouse model. Approach: We introduced the optical attenuation coefficient decorrelation (OACD) method for OCTA, effectively reducing tail artifacts from superficial cortex vessels. This method was used to visualize and quantitatively analyze deep cortical microvasculature in vivo in a mouse model of AD. Results: The OACD method significantly reduced superficial vessel artifacts, leading to clearer imaging of the deep cortical vasculature. Quantitative analysis revealed that changes in the deep cortical microvasculature were more pronounced than in the superficial vasculature, suggesting a more direct involvement of the deep vessels in AD progression. Conclusions: The proposed OACD method enhances OCTA imaging by reducing tail artifacts from superficial vessels, facilitating improved assessment of deep cortical microvasculature. These findings suggest that deep cortical vascular changes may play a key role in the pathogenesis of AD, offering potential insights for early detection and monitoring of AD progression. © 2025 SPIE. All rights reserved.

关键词： Optical coherence tomography

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Decoupling composite modulation models in frequency-scanning interferometry for dynamic absolute ranging using a multi-synchrosqueezing transform

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Applied Optics 2025年第13期64卷 3554-3560页

作者： Deng, Zhongwen Chen, Wenjun Li, Lin Sun, Haifeng Zhang, Hengkang Shen, Lirong Gong, Hai Wang, Li Li, Xiaoping School of Space Science and Technology Xidian University Xi’an710071 China Space Optoelectronic Measurement and Perception Lab Beijing Institute of Control Engineering Beijing100190 China Huzhou Institute of Zhejiang University Zhejiang University Huzhou313000 China

We present a decoupling method for a composite modulation model in a simple frequency-scanning interferometry (FSI) scheme for dynamic absolute ranging. The method employs a multi-synchrosqueezing transform for data processing, enabling accurate and robust estimation of the instantaneous frequency and effective separation of target motion from the interference signal within the composite modulation model. Both simulations and experimental results demonstrate the proposed FSI scheme’s superior dynamic ranging performance. In vibration target measurements at a standoff distance of 15 m, the system achieved a relative error of less than 9.78% for amplitude and 1.60% for frequency in the range of 100–1000 Hz in vibration target measurement. © 2025 Optica Publishing Group.

关键词： Interferometry

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Deep Reinforcement Learning Vehicle Visual Navigation Based on Attention Mechanism and LSTM 16th

Deep Reinforcement Learning Vehicle Visual Navigation Based ...

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16th International Conference on Modelling, Identification and control, ICMIC 2024

作者： Yu, Zhentao Zhang, Guojun Li, Ruoting Liu, Peng School of Instrument and Electronics North University of China Taiyuan030051 China The State Key Laboratory of Dynamic Measurement Technology North University of China Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China

ISBN: (纸本)9789819617760

This paper studies the visual navigation of vehicles in unknown environments. Due to the lack of global information, the accuracy of graphical navigation method will be decline. We propose a double LSTM attention navigation model to improve vehicle navigation performance. The model utilizes a dual-channel LSTM network for processing time series data and introduces an attention mechanism into the asynchronous dominant actor critic algorithm to mitigate the reward sparsity problem. Through comparative with various models on the AI2-THOR platform, the enhanced model improves the cumulative rewards and success rates of visual navigation. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Deep reinforcement learning

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Graph Convolutional Networks with Visual Masking and Social Interaction Models for Pedestrian Trajectory Prediction

Graph Convolutional Networks with Visual Masking and Social ...

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Consumer Electronics and Computer engineering (ICCECE), 2021 IEEE International Conference on

作者： Guopei Zeng Luping Wang School of Electronics and Communication Engineering Sun Yat-sen University Shenzhen China Zhejiang Aerospace Runbo Measurement and Control Technology Co. Ltd Zhejiang China

ISBN: (数字)9798331533113

ISBN: (纸本)9798331533120

Pedestrian trajectory prediction is critical for autonomous driving, crowd analysis, and urban surveillance. To address insufficient interaction modeling in existing methods, we propose two enhancements. First, a field-of-view(FoV)-aware masking mechanism filters irrelevant interactions by dynamically adjusting to pedestrian distances and motion directions. Second, we introduce a more detailed modeling of the influence among pedestrians, replacing the traditional modeling based on reciprocal of distance. This model, integrated with FoV masks to construct sparse adjacency matrices for graph convolution. A temporal convolutional network then predicts trajectories as bivariate Gaussian distributions. Evaluations on ETH and UCY benchmarks demonstrate the effectiveness of our method.

关键词： Analytical models Visualization Pedestrians Filters Surveillance Predictive models Benchmark testing Vectors Trajectory Sparse matrices

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Data-Knowledge-Driven for UAV Swarm communication Interference Recognition

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IEEE Internet of Things Journal 2025年

作者： Wang, Bin Li, Aiping Wang, Anyi Sun, Yanjing Li, Song Xi'an University of Science and Technology School of Communication and Information Engineering Xi'an710054 China School of Information and Control Engineering China University of Mining and Technology Xuzhou221116 China

Achieving high-precision interference recognition for unmanned aerial vehicle (UAV) swarm communications in complex electromagnetic environments is of great significance for developing efficient anti-interference schemes and improving the security of UAV swarm communication. Although deep learning-based interference recognition methods for UAV swarm communication can achieve good recognition performance, they usually rely on a large number of high-quality labelled samples and only consider a single representation of the interference signal as input. This leads to low accuracy and poor robustness of interference recognition in scenarios with changing electromagnetic environments or insufficient samples. To address these issues, this paper proposes a data-knowledge-driven method for UAV swarm communication interference recognition. A dual-input interference recognition network (DIRNet) with a few model parameters is designed, incorporating deep features extracted based on the data-driven approach and manual features designed based on expert knowledge. Simulation experiments are conducted under sufficient-sample, cross-environment, and insufficient-sample scenarios. The results demonstrate that the proposed UAV swarm communication interference recognition method not only improves the recognition accuracy of interference signals under these conditions. Moreover, it also shows good robustness in complex dynamic environments. © 2014 IEEE.

关键词： Aircraft communication

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Radio Signal Modulation Pattern Recognition Based on Time-Frequency Adaptive Decomposition and Hybrid Neural Network

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IEEE Internet of Things Journal 2025年

作者： Dai, Shaogang Xu, Jianwei Zhou, Huaji Hangzhou Dianzi University School of communication engineering Hangzhou310018 China Science and Technology on Communication Information Security Control Laboratory Jiaxing314000 China

Automatic modulation classification of electromagnetic signals has an important role in the field of signal processing. The current methods for identifying the modulation pattern of radio signals mainly used end-to-end neural network models, which suffered from the problems such as network redundancy and non-interpretability. In order to solve the above problems, this paper proposes a radio signal modulation pattern recognition method based on time-frequency adaptive decomposition and hybrid neural network. The discrete wavelet decomposition is used to decompose the original radio signals into adaptive high and low frequencies. Then feed the resulting high and low frequency portions into a multi-channel hybrid neural network (MHNN) for training to obtain the classification results. In the discrete wavelet decomposition process, the number of decomposition layers and the decomposition threshold are determined adaptively according to the difference of SNRs. In the design of multi-path hybrid neural network structure, the signal characteristics of high and low frequency parts are considered comprehensively. The network channels with different convolutional kernel scales and different layers are constructed to reduce the number of model parameters and training time while ensuring the classification accuracy. Using RadioML2016.10a dataset for experiments, the recognition accuracy of MHNN is prior to the end-to-end deep learning methods, which can achieve the accuracy of 92.30% and the F1 of 91.84% when SNR=2dB. It shows that the proposed radio signal modulation pattern recognition method based on the time-frequency adaptive decomposition and hybrid neural network can balance the accuracy and lightweight. Moreover, it has certain interpretability for the original input data, distinguishing the main body and details of the signal by dividing the signal into high and low frequencies, which provides a new idea for the interpretability of deep convolutional neural networks. ©

关键词： Signal modulation

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Optimization of working conditions in solid oxide electrolysis cell based on deep neural network and slime mold algorithm

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International Journal of Hydrogen Energy 2025年 135卷 172-181页

作者： Zhang, Jiawei Wang, Zhuo Li, Ming Liu, Guoqiang Zhang, Lin Xu, Yuanwu Li, Xi School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China Marine Engineering College Dalian Maritime University Dalian116026 China School of Information Science and Engineering Wuhan University of Science and Technology Wuhan430081 China Shenzhen Huazhong University of Science and Technology Research Institute Shenzhen518055 China Department of Early Warning Technology Air Force Early Warning Academy Wuhan430019 China Belt and Road Joint Laboratory on Measurement and Control Technology Huazhong University of Science and Technology Wuhan430074 China

Solid oxide electrolysis cell (SOEC) are a highly efficient hydrogen production technology, but thermal stresses induced by temperature gradient under high temperature may accelerate performance degradation and cause equipment damage. Optimizing operating parameters to balance efficiency and thermal safety is critical to addressing these challenges. Existing optimization methods primarily rely on numerical simulations and experimental validations, but their high time and economic costs limit practical applications. To overcome this challenge, this study proposes a performance optimization method that, unlike conventional models, considers thermal safety constraints and rapidly determines optimal SOEC operating conditions with varying power inputs. By integrating the deep neural network with a one-dimensional multiphysics field model, the method leverages data generated by complex multiphysics field model to train the network and systematically analyze the effects of different operating conditions on SOEC stack power and efficiency. Subsequently, the slime mold algorithm is employed to efficiently optimize operating parameters under power and thermal constraints, achieving maximum SOEC efficiency and providing theoretical support for future dynamic control strategies. © 2025 Hydrogen Energy Publications LLC

关键词： Molds

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Electrical properties and thermally stable property of lead-free BNT-BT-KNN ceramics at the morphotropic phase boundary region

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Ceramics International 2025年

作者： Mu, Yan Wang, Junjun Wang, Bingsen Jiao, Yufang He, Jian Liu, Meng Sun, Che Yuan, Shaoyang Liu, Danqing Yang, Wenlong Wu, Fengmin School of Science Harbin University of Science and Technology Harbin150080 China School of Material Science and Chemical Engineering Harbin University of Science and Technology Harbin150080 China School of Measurement and Communication Engineering Harbin University of Science and Technology Harbin150080 China

Lead-free BNT-BT based ceramics exhibit significant potential for achieving high piezoelectric performance. However, it is an effective strategy to achieve a morphotropic phase boundary (MPB) region with good thermal stability remains challenging. Herein, the incorporation of K0.5Na0.5NbO3 into the Bi0.5Na0.5TiO3-BaTiO3 binary system was explored, leading to a significant enhancement in both piezoelectric performance and thermal stability. The ternary BNT-BT-KNN ceramic system was systematically investigated with respect to its phase diagram, crystal structure, electrical properties, and relaxation behavior. It was observed that both the grain size and domain size gradually decrease as the KNN content increases. Remarkable electromechanical coupling factor (kp = 0.217), exceptionally high piezoelectric properties (d33 = 105 pC/N), and improved (Td = 164 °C) were achieved at BNT-BT-0.01KNN ceramics. The phase diagram of BNT-BT-KNN has been established, offering a comprehensive roadmap for the development of high-performance piezoelectric ceramics with enhanced piezoelectric responses. This work may facilitate potential applications in piezoelectric actuators. © 2025 Elsevier Ltd and Techna Group S.r.l.

关键词： Piezoelectric ceramics

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Dynamic Programming Based Strategies of Future Crop Plant 24

Dynamic Programming Based Strategies of Future Crop Plant

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Proceedings of the 2024 5th International Conference on Big Data Economy and Information Management

作者： Beining Zhang Xueying Wang Yongkang Deng Kai Chen Qinglang Liu Measurement and Control Technology and Instruments School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan China School of Nuclear Technology and Automation Engineering Chengdu University of Technology Chengdu Sichuan China

ISBN: (纸本)9798400711862

In this paper, we develop a decision model based on dynamic programming to create an optimal crop plan for 2024-2030 in rural northern China. Considering different marketing scenarios, uncertainties, and crop substitutions and complementarities, this paper solves the problem with three sub-problems. First, the optimal cropping plan for 2024-2030 is calculated based on the cropping scenario for 2023. It is assumes that expected sales, cropping costs, yields and marketing prices are stable. Secondly, a multi-objective planning model was developed. It aimed at profitability and stability and took into account uncertainties. Finally, the planting plan was further optimised and it considered crop substitutability and complementarity, and the relationship between expected sales and selling prices and planting costs. Through dynamic planning and Monte Carlo simulation, this paper takes into account uncertainty and crop relationships and it solves the optimal crop planting plan for the years 2024-2030 with a maximum profit of about $7.737 million. Therefore, this paper proposes a decision model based on dynamic programming and it takes into account various factors of the planting plan for the next seven years. An effective method is provided for solving the crop planting strategy problem in sustainable agricultural development.

关键词： Dynamic Programming

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