检索结果-内蒙古大学图书馆

21st International Conference on Networking, Sensing and control, ICNSC 2024

作者： Yan, Wenxiao Wang, Jiayu Fan, Liqun Liu, Qiusheng Jia, Qingdong Wang, Yihan Liu, Hongjie Shenzhen Metro Group Co. Ltd. Shenzhen518026 China Shenzhen Municipal Design and Research Institute Co. Ltd. Shenzhen518029 China Traffic control Technology Co. Ltd. Beijing100070 China School of Automation and Intelligence Beijing Jiaotong University Beijing100044 China Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing100044 China

ISBN: (纸本)9798350365221

With the rapid growth of transportation passenger flow in recent years, the research direction of Virtual Coupling (V C) in railways has become hot. V C increases capacity by tracking shorter distances. The traditional train emergency braking (EB) model can no longer meet the high operating efficiency requirements of VC. VC urgently needs a more efficient safety EB model. Therefore, to improve efficiency, a measurement of the EB model's rationality is defined considering safety and efficiency. Based on the defined model measurement, an EB model optimization method and a new EB model are proposed. The new model is closer to the actual braking process of the train and ensures safety from the distance level. That is, the braking trajectory of the train calculated by the new model is closer to the actual braking trajectory of the train, and the two braking trajectories satisfy the specific safety constraints. Finally, the safety and efficiency of the new model were verified through simulation experiments. The new model can avoid train collisions during the entire EB process. Experimental results show that compared with the traditional old EB model of VC based on space-time interval [1], the new model's minimum safe distance (MSD) between two VC train units can be reduced by 39%. © 2024 IEEE.

关键词： Railroad accidents

来源：评论

学校读者我要写书评

暂无评论

高雷诺数下覆羽控制直翼型流动分离的位置影响实验

引用

Acta Mechanica Sinica 2024年第4期40卷 125-135页

作者：巩绪安马兴宇姜楠 Department of Mechanics School of Mechanical EngineeringTianjin UniversityTianjin300354China National Key Laboratory of Aerodynamic Noise Control China Aerodynamics Research and Development CenterSichuan621000China National Key Laboratory of Science and Technology on Aerodynamic Design and Research School of AeronauticsNorthwestern Polytechnical UniversityXi’an710072China State Key Laboratory of Rail Transit Vehicle System Southwest Jiaotong UniversityChengdu610031China

本文在二维机翼上安装了仿生的覆羽结构,在5.0×105的雷诺数下研究它们控制前缘流动分离的效果.覆羽是指覆盖在鸟类翅膀上的多排短而柔软的带有锯齿状尖端的羽毛.它们覆盖在长而硬的飞羽边缘,使鸟的翅膀保持流线型.当流动分离发生时... 详细信息

本文在二维机翼上安装了仿生的覆羽结构,在5.0×105的雷诺数下研究它们控制前缘流动分离的效果.覆羽是指覆盖在鸟类翅膀上的多排短而柔软的带有锯齿状尖端的羽毛.它们覆盖在长而硬的飞羽边缘,使鸟的翅膀保持流线型.当流动分离发生时,这些柔性锯齿形的人工覆羽在来流的激励下能够自适应地变形和颤振.在风洞实验中,本文使用高时间分辨率的粒子图像测速仪(PIV)来测量机翼上表面的速度场,并使用热线风速仪来测量尾流中的高频湍流脉动.安装在20%弦长处的人工覆羽在前缘分离流的激励下会自适应地大幅翻转.安装在80%弦长处的覆羽则会被尾缘卷起的脱落涡向上抬起,并在准平衡状态下轻微摆动.这两类人工覆羽均能够有效地控制流动分离,并减小尾流的湍流脉动强度.

关键词：流动分离风洞实验分离流湍流脉动高时间分辨率热线风速仪高雷诺数锯齿形

来源：评论

学校读者我要写书评

暂无评论

Simulation and analysis of far-field single-photon response model in Geiger avalanche mode 7

Simulation and analysis of far-field single-photon response ...

引用

7th Global Intelligent Industry Conference, GIIC 2024

作者： Ou, Hengchang Ma, Le Jiang, Peng Wang, Pengfei Lu, Jie Zhou, Xin Sun, Jianfeng National Key Laboratory of Laser Spatial Information Institute of Opto-Electronic Harbin Institute of Technology Heilongjiang Harbin150001 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China Beijing Institute of Remote Sensing Beijing100854 China 44th Research Institute China Electronics Technology Group Corporation Chongqing400060 China Research Center for Space Optical Engineering Harbin Institute of Technology Heilongjiang Harbin150001 China Zhengzhou Research Institute of Harbin Institute of Technology Zhengzhou Henan450046 China

ISBN: (纸本)9781510682986

Poisson distribution model is the basis of data analysis for GM-APD lidar, but it is only applicable to the mirror reflected target under ideal conditions, and cannot accurately describe the photon triggering process of actual GM-APD lidar detection. For the actual target with rough surface, the negative binomial distribution with M as the parameter can describe the photon distribution model more accurately. In order to solve this problem, this paper compares and analyzes Poisson distribution triggering model and the negative binomial distribution triggering model that conforms to the triggering situation of the echo signal of the target with rough surface, considering the differences in the triggering probability under different noise and signal levels. The results show that the trigger probability curve corresponding to the trigger model based on negative binomial distribution is lower in peak value, wider in bottom value and fatter overall than that of the trigger model based on Poisson distribution, and the difference between the two is more prominent under the conditions of low noise level and high signal level. This study has guiding significance for the signal extraction research based on different surface echoes. © 2024 SPIE.

关键词： Photons

来源：评论

学校读者我要写书评

暂无评论

Low-Earth Orbital Lifetime Prediction Based on Parameter Sensitivity Analysis and Deep Learning

引用

IEEE Transactions on Aerospace and Electronic systems 2025年

作者： Chen, Shun-Yi Yang, Zhen Wang, Hua Lu, Lin State Key Laboratory of Space System Operation and Control Changsha410073 China National University of Defense Technology College of Aerospace Science and Engineering Changsha410073 China China Astronaut Research and Training Center Beijing100094 China

This article proposes an intelligent prediction method for the orbital lifetime of resident space objects (RSOs) in low Earth orbit. This method is intended to satisfy the computational efficiency and accuracy requirements when predicting the large-scale RSOs. It helps to analyze the distribution and uncertainty of RSOs' lifetime. First, based on the semi-analytical orbit prediction model, a parameter sensitivity analysis method for the orbital lifetime is proposed, and the parameters that affect the orbital lifetime are investigated. Second, using a fully connected neural network, an orbital lifetime prediction model is established based on deep learning. The hyperparameters of the deep learning model are optimized using the black-winged kite algorithm. Finally, by combining the sensitivity analysis with the deep learning model, an intelligent prediction method is proposed for the rapid prediction of orbital lifetime. Simulation results show that the proposed intelligent prediction method accurately determines the orbital lifetime of RSOs and has higher prediction efficiency than the traditional prediction method. The research results provide an effective calculation and analysis tool for predicting the orbital lifetime of large-scale RSOs in the future. © 2025 IEEE.

关键词： Prediction models

来源：评论

学校读者我要写书评

暂无评论

A SINS Error Correction Approach Based on Dual-Threshold ZV Detection and Cubature Kalman Filter

A SINS Error Correction Approach Based on Dual-Threshold ZV ...

引用

2023 IEEE International Conference on systems, Man, and Cybernetics, SMC 2023

作者： Xu, Ruijie Chen, Shichao Sun, Wenqiao Lv, Yisheng Luo, Jialiang Tang, Ying Institute of Automation Chinese Academy of Sciences College of Information Science & Technology Beijing University of Chemical Technology The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Transportation and Economics Research Institute The Center of National Railway Intelligent Transportation System Engineering and Technology China Academy of Railway Sciences Corporation Limited Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Institute of Automation Chinese Academy of Sciences China University of Geosciences Beijing School of Information Engineering The State Key Laboratory for Management and Control of Complex System State Key Laboratory of Multimodal Artificial Intelligence Systems Beijing China Rowan University Department of Electrical and Computer Engineering Glassboro United States

ISBN: (纸本)9798350337020

Global Navigation Satellite systems (GNSS) can provide real-time positioning information for outdoor users, but cannot for indoor scenarios or heavily occluded outdoor scenarios. Strap-down Inertial Navigation system (SINS) are widely used to locate people in complex interior or heavily occluded outdoor scenarios due to its light weight and low power consumption. However, IMU of SINS are noisy, and the sampling data error is large, which is a divergence of the error with time. Therefore, it will generate a positioning accumulation error, which affects the final positioning accuracy. The problem of cumulative IMU errors is usually dealt with by Zero-Velocity Update (ZUPT). The zero-velocity detection part of basic ZUPT method usually uses a single threshold to determine the gait of pedestrian, which often has the problem of gait misjudgment and omission. To address these problems, this paper proposes a composite conditional detection method to solve the problem of misjudgment in the zero-velocity interval. In addition, we redesign the zero-velocity update algorithm and uses the Cubature Kalman filter (CKF) for pedestrian positioning error correction. The experimental results demonstrate that the proposed ZUPT method based on dual-threshold detection can better detect the interval between pedestrian motion and stationery than ones with single threshold. The zero-velocity update algorithm based on CKF has higher performance than conventional EKF and UKF methods, which constrains the cumulative error of SINS to about 0.2% of the whole walking distance. © 2023 IEEE.

关键词： Cubature Kalman Filter Inertial Pedestrian Navigation Zero-Velocity Update

来源：评论

学校读者我要写书评

暂无评论

Intelligent Train Tracking control Based on Monte Carlo Tree Method

Intelligent Train Tracking Control Based on Monte Carlo Tree...

引用

International Conference on Intelligent Transportation

作者： Zixu Zhao Jidong Lv Boyuan Zhou Wanli Lu Ming Chai Hongjie Liu National Engineering Research Center Of Rail Transportation Operation And Control System Beijing JiaoTong University Beijing China Institute of Communication and Signalling The Center of National Railway lntelligent Transportation System Engineering and Technology Chinese Academy of Railway Science CARS Beijing China

Train tracking distance control is critical to ensure safety and enhance the transportation capacity of urban rail transit. The widely used control methods relying on the dynamics model of trains may not achieve ideal performance because of the inaccuracy of the dynamics model and delay of control commands execution, especially in the Urban Rail Transit scenarios. To overcome these limitations, this paper presents an intelligent tracking control method NN-MCTS combining a deep learning model with Monte Carlo Tree Search (MCTS) to cope with the tracking distance minimization. The deep learning model adopts a neural network (NN) to characterize the relationship between control command and actual train dynamic, while MCTS regards the control problem as a decision-making process leveraging the trajectory output of NN to obtain the reward of the corresponding control sequence. We extracted 20000 actual state trajectory segments of trains of Chengdu Metro Line 8 to train the deep-learning model, and additionally continuous trajectories as the known state of the train ahead to verify the effectiveness of the search algorithm. Experiments show the advantages of the proposed method in accuracy and stability compared with PID and real-time compared with MPC method.

关键词：

来源：评论

学校读者我要写书评

暂无评论

A Computationally Efficient Target Localization Algorithm in Underwater Wireless Sensor Networks 8

A Computationally Efficient Target Localization Algorithm in...

引用

8th International Conference on Computer and Communication systems, ICCCS 2023

作者： Mei, Xiaojun Wu, Huafeng Han, Dezhi Chen, Xinqiang Xian, Jiangfeng Han, Bing College of Information Engineering Shanghai Maritime University Shanghai China National Engineering Research Center of Ship & Shipping Control System Shanghai China Shanghai Ship and Shipping Research Institute Co. Ltd Shanghai China Merchant Marine College Shanghai Maritime University Shanghai China Institute of Logistics Science and Engineering Shanghai Maritime University Shanghai China Wuhan University of Technology Hubei Key Laboratory of Inland Shipping Technology Wuhan China

ISBN: (纸本)9781665456128

Localization is of paramount importance for underwater wireless sensor networks (UWSNs). However, achieving accurate location is infeasible, especially in the highly dynamic underwater environment. The acoustic signal may suffer hybrid loss, including path and absorption loss, which dramatically degrades the localization accuracy. Even though some localization methods have been proposed, the trade-off between accuracy and computational complexity cannot be well balanced. In this context, the paper proposes a computationally efficient method that investigates the localization problem in the alternating nonnegative constrained least squares (ANCLS) framework after linearization operation. The potential solutions are divided into two groups, wherein the optimal one is filtered under the constraint by exchanging the variables from one to another. A block principal pivoting-based localization (BPPL) method is then presented to estimate the target's location. Simulations reveal that the computational complexity and the localization accuracy of BPPL are competitive compared with the state-of-the-art methods in different scenarios. © 2023 IEEE.

关键词： Computational complexity

来源：评论

学校读者我要写书评

暂无评论

Combining Transformer and Convolutional Neural Network for Smoke Detection 9th

Combining Transformer and Convolutional Neural Network for...

引用

9th International Forum on Digital Multimedia Communication, IFTC 2022

作者： Gong, Yafei Lian, Xinkang Ma, Xuanchao Xia, Zhifang Zhou, Chengxu Faculty of Information Technology Beijing University of Technology Beijing China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Laboratory of Smart Environmental Protection Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China National Information Center Beijing China School of Electronic and Information Engineering Liaoning University of Technology Liaoning Jinzhou China Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Dalian China

ISBN: (纸本)9789819908554

Smoke detection plays a crucial role in the safety production of petrochemical enterprises and fire prevention. Image-based machine learning and deep learning methods have been widely studied. Recently, many works have applied the transformer to solve problems faced by computer vision tasks (such as classification and object detection). To our knowledge, there are few studies using the transformer structure to detect smoke. In order to research the application potential and improve the performance of the transformer in the smoke detection field, we propose a model consisting of two transformer encoders and a convolutional neural network (CNN) module. The first transformer encoder can be used to establish the global relationship of an image, and the CNN structure can provide additional local information to the transformer. The fusion of global information and local information is conducive to the second transfer encoder to make better decisions. Experiments results on large-size dataset for industrial smoke detection illustrate the effectiveness of the proposed model. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Smoke

来源：评论

学校读者我要写书评

暂无评论

Screw thermal characteristics analysis and error prediction considering the two-dimensional heat transfer structure

Research Square

引用

research Square 2021年

作者： Chen, Yu Chen, Jihong Xu, Guangda National Numerical Control System Engineering Research Center Huazhong University of Science and Technology Wuhan430074 China

Thermal error is a common problem in machine tool processing. It is usually proposed to take the screw as one-dimensional rod heat transfer system to solve the thermal error of screw by establishing thermal characteristic equation. This method regards the temperature on the nut as the temperature of the screw contact surface without considering the heat transfer distance between screw and the mounting point on the nut. To solve this problem, this paper takes the screw-nut feed system as a two-dimensional heat transfer structure considering both screw and nut. In addition, to solve the low convergence speed and easy fall into local optimum problem in optimal parameter identification. The improved particle swarm optimization algorithm (IPSOA) is proposed to identify the boundary parameters of thermal characteristics equations. Finally, the temperature and thermal error of the screw-nut feed system is calculated by the identified results. Experiments under different working condition show that the maximum prediction residual error of the two-dimensional method (TDM) is less than 7.2 um and the simulating prediction accuracy can reach 86.2%. Besides, compared with one-dimensional model (ODM), TDM has a higher predict accuracy which verifies the effectiveness of the proposed method. © 2021, CC BY.

关键词： Particle swarm optimization (PSO)

来源：评论

学校读者我要写书评

暂无评论

Condition Monitoring for Railway Point Machines Based on Sound Analysis and Support Vector Machine

引用

Chinese Journal of Electronics 2020年第4期29卷 786-792页

作者： SUN Yongkui CAO Yuan XIE Guo WEN Tao School of Electronic and Information Engineering Beijing Jiaotong University National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi'an University of Technology

Railway point machines(RPMS) are one of the key equipments in the railway system to switch different routes for the *** monitoring for RPMs is a vital measure to keep train operation safe and *** convenience and low cost into consideration, a novel intelligent condition monitoring method for RPMs based on sound analysis is ***-domain and frequency-domain features are obtained,and normalized using z-score standardization method to eliminate the influences of different *** particle swarm optimization(BPSO) is utilized to select the most significant discrimination feature *** effects of the selected optimal features are verified using Support vector machine(SVM), 1-Nearest neighbor(1 NN), Random forest(RF), and Naive Bayes(NB).Experiment results indicate SVM performs best on identification accuracy and computing cost compared with the other three *** identification accuracies on normal switching and reverse switching processes reach100% and 99.67%, respectively, indicating the feasibility of the proposed method.

关键词： Condition monitoring Sound analysis Railway point machines(RPMs) Binary particle swarm optimization(BPSO) Support vector machine(SVM)

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：