检索结果-内蒙古大学图书馆

UNIfied Conference of International Workshop on Defence Applications of Multi-Agent Systems, DAMAS 2023, International Conference on Maintenance Engineering, IncoME-V 2023, International conference on the Efficiency and Performance Engineering Network, TEPEN 2023

作者： Wu, Yangbiao Zhang, Chao Wang, Jianguo Xu, Shuai School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014010 China

ISBN: (纸本)9783031494123

Dynamic modeling of rolling bearing fault is an important method in fault mechanism research. However, existing dynamic models only consider the influence of fault size and bearing speed on the instantaneous impact force, failing to fully reflect the actual contact conditions. To address this, this paper introduces the influence of contact deformation on the instantaneous impact force based on the existing dynamic model. At the same time, focusing on the outer ring fault of the bearing, a transient impact force excitation function considering contact deformation is proposed. The dynamic model of a deep groove ball bearing's outer ring fault is established based on the Hertz contact theory. Finally, the effectiveness of the proposed model is verified through simulations and comparative experiments. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Dynamic models

来源：评论

学校读者我要写书评

暂无评论

A Multi-Task Learning Method for Communication Emitter Individual Identification 16

A Multi-Task Learning Method for Communication Emitter Indiv...

引用

16th International Conference on Communication Software and Networks, ICCSN 2024

作者： Liang, Tianyi Liu, Sichen Zhang, Lan Zhang, Biao No.36 Research Institute of China Electronics Technology Group Corporation National Key Laboratory of Electromagnetic Space Security Jiaxing China Xi'an Jiaotong-Liverpool University School of Advanced Technology Department of Intelligent Science Suzhou China

ISBN: (纸本)9798331506322

This paper proposes a multi-task convolutional neural network identification method based on raw I/Q data, which is used to solve modulation identification and specific emitter identification in communication emitters. The modulation identification and communication emitter identification as two interrelated learning tasks. This method extracts features from raw I/Q data through a deep neural network with shared parameters. Two branch networks with different structures are then used for classification and identification, while joint optimization training is conducted on these two tasks to facilitate mutual learning. The paper employs 15 different USRPs for validation, conducting both single-task and multi-task model analyses to verify the effectiveness of the multi-task architecture. The results demonstrate that, while ensuring the accuracy of modulation identification, the multi-task model improves the identification rate of communication emitters by 1.1% compared to the single-task model identification method. © 2024 IEEE.

关键词： Convolutional neural networks

来源：评论

学校读者我要写书评

暂无评论

Research on Data Security Risk Analysis Method of intelligent and Connected Vehicles Based on Data Asset 24

Research on Data Security Risk Analysis Method of Intelligen...

引用

24th COTA International Conference of Transportation Professionals: Resilient, intelligent, Connected, and Lowcarbon Multimodal Transportation, CICTP 2024

作者： Wang, Liyong Jin, Long Ji, Haojie Chen, Yupeng He, Peng Wang, Jingyan Fang, Junzhe State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science & Technology Univ. Beijing China China Automotive Engineering Research Institute Co. Ltd. Chongqing China

ISBN: (数字)9780784485484

ISBN: (纸本)9780784485484

The diversity and complexity of typical scenarios of intelligent and connected vehicles (ICVs) bring serious risks to the data security of vehicles. To avoid the potential data security risks of ICVs effectively, this paper proposes a data risk analysis method for vehicles based on the characteristics of data assets. Considering the similarity between the internet of things (IoT) system and the internet of vehicles system, this method organizes the data assets of the vehicles combined with the layered architecture of the IoT system. The data security risks of vehicles are analyzed from the aspects of threat analysis, vulnerability analysis, and risk quantification. The risk points of vehicles are analyzed, so as to provide model design and verification methods for data security risk analysis of ICVs. © ASCE.

关键词：

来源：评论

学校读者我要写书评

暂无评论

Novel camera self-calibration method with clustering prior and nonlinear optimization from an image sequence

引用

Multimedia Tools and Applications 2024年第42期83卷 90553-90572页

作者： Jiang, Xiaohui Zhu, Haijiang An, Ning Xie, Binjian Wei, Hao Tang, Fulin Wu, Yihong College of Information Science and Technology Beijing University of Chemical Technology Beijing100029 China State Key Laboratory of Multimodal Artificial Intelligence Institute of Automation Chinese Academy of Sciences Beijing100190 China State Key Laboratory of Intelligent Coal Mining and Strata Control Beijing100013 China Research Institute of Mine Big Data China Coal Research Institute Beijing100013 China

In many 3D reconstruction, Simultaneous Localization and Mapping (SLAM), AR, and VR systems, the camera focal length constantly changes slightly due to loose hardware, causing their performances to deteriorate. To address this problem, we propose a novel camera self-calibration method that incorporates clustering prior and nonlinear optimization. By leveraging the Kruppa equation, we roughly estimate multiple analytical solutions for the camera focal length from an image sequence. Then, robust clustering based on the Gaussian mixture model is introduced to obtain an initial camera focal length value. Finally, a nonlinear cost function is proposed to refine the initial camera focal length value. Extensive experimental results on simulation and real data demonstrate that the proposed method outperforms the state-of-the-art methods, exhibiting good robustness and high accuracy. Moreover, the proposed method can be used in real-time videos for SLAM systems. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Cost functions

来源：评论

学校读者我要写书评

暂无评论

Face anti-spoofing based on multi-modal and multi-scale features fusion

引用

The Journal of China Universities of Posts and Telecommunications 2022年第6期29卷 73-82页

作者： Kong Chao Ou Weihua Gong Xiaofeng Li Weian Han Jie Yao Yi Xiong Jiahao School of Big Data and Computer Science Guizhou Normal UniversityGuiyang 550025China Special Key Laboratory of Artificial Intelligence and Intelligent Control of Guizhou Province Guizhou Institute of TechnologyGuiyang 550003China Guizhou Science and Technology Information Center Department of Science and Technology of Guizhou ProvinceGuiyang 550002China

Face anti-spoofing is used to assist face recognition system to judge whether the detected face is real face or fake face. In the traditional face anti-spoofing methods, features extracted by hand are used to describe the difference between living face and fraudulent face. But these handmade features do not apply to different variations in an unconstrained environment. The convolutional neural network(CNN) for face deceptions achieves considerable results. However, most existing neural network-based methods simply use neural networks to extract single-scale features from single-modal data, while ignoring multi-scale and multi-modal information. To address this problem, a novel face anti-spoofing method based on multi-modal and multi-scale features fusion(MMFF) is proposed. Specifically, first residual network(Resnet)-34 is adopted to extract features of different scales from each modality, then these features of different scales are fused by feature pyramid network(FPN), finally squeeze-and-excitation fusion(SEF) module and self-attention network(SAN) are combined to fuse features from different modalities for classification. Experiments on the CASIA-SURF dataset show that the new method based on MMFF achieves better performance compared with most existing methods.

关键词： face anti-spoofing multi-modal fusion multi-scale fusion self-attention network(SAN) feature pyramid network(FPN)

来源：评论

学校读者我要写书评

暂无评论

Variational Mode Decomposition Guided by Time-Frequency Domain Difference Information 1

引用

TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： Fei, Hongbo Zhang, Chao Xu, Shuai Zhang, Jing Wu, Le School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014010 China

ISBN: (数字)9783031694837

ISBN: (纸本)9783031694820

To address the difficulty of extracting fault features of rolling bearings in noisy environments, and the issues where the main effects of Variational Mode Decomposition (VMD) are influenced by the decomposition level and penalty factor, a method based on time-frequency domain difference information-guided Variational Mode Decomposition for rolling bearing fault feature extraction is proposed. Initially, the constraints in VMD decomposition are utilized as discrimination conditions, constructing similarity coefficient differences and energy difference ratios as convergence criteria to find the optimal parameter combination. The IMF component with the maximum envelope peak factor is selected for envelope demodulation to determine the fault type. Simulation and experimental results demonstrate that this method can extract fault features from noise interference, confirming its effectiveness. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Variational mode decomposition

来源：评论

学校读者我要写书评

暂无评论

Composite Fault Diagnosis of VMD-FastICA Bearing Based on Parameter Optimization

Composite Fault Diagnosis of VMD-FastICA Bearing Based on Pa...

引用

作者： Zhang, Chao Fei, Hongbo Wu, Le Da, L.V. School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014000 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014000 China

ISBN: (纸本)9783031494123

Rolling bearings are one of the most commonly used bearing components in rotating machinery and are also one of the most prone to failure. Therefore, the diagnosis of the condition of rolling bearings is of significant importance for safety and economy. In practical engineering, bearings are often interfered with by vibration signals from other components, which makes it difficult to extract the fault characteristic information of bearings due to noise or other signals. In this paper, to address the problem of difficulty in extracting compound fault characteristic information of rolling bearings under strong noise background, a feature extraction method based on sparrow algorithm optimized VMD parameters and energy entropy and envelope entropy mean minimization is proposed, and combined with FastICA to achieve signal separation, denoising, and feature enhancement. This method is applied to the fault feature extraction of simulated signals, achieving denoising and feature enhancement of separated signals. The results of simulation and experimental analysis demonstrate the feasibility of this method. Furthermore, a comparison with other methods is conducted to validate the superiority of this method. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Roller bearings

来源：评论

学校读者我要写书评

暂无评论

Research on Surface Defect Detection technology of Railway Tracks Based on Image Processing

Research on Surface Defect Detection Technology of Railway T...

引用

作者： Liu, Guiyi Zhang, Chao Wang, Jianguo Chi, Yuliang Xu, Shuai School of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014000 China Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems in Inner Mongolia Autonomous Region Baotou014000 China

ISBN: (纸本)9783031494123

Railroad transportation is an essential component of national economic development, providing crucial support for safe and efficient transportation. The quality of rail infrastructure directly impacts the efficiency and safety of train operation. Therefore, the timely and accurate detection of rail surface defects is a critical element of railroad operation and safety management. This paper examines the technology of railroad track surface detection and proposes a novel defect detection method based on image processing. The proposed method utilizes a high-resolution digital camera to capture images of the rail surface and eliminates noise and lighting effects using pre-processing techniques. Next, a feature extraction algorithm is employed to extract the features of rail surface defects, and is used to classify and identify them. Experimental results demonstrate that the proposed method accurately detects various defects on the rail surface. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Surface defects

来源：评论

学校读者我要写书评

暂无评论

Fog Images Generation About Unmanned Surface Vessels with Improved Generative Adversarial Network 42

Fog Images Generation About Unmanned Surface Vessels with Im...

引用

42nd Chinese control Conference, CCC 2023

作者： Tang, Yifeng Huang, Zhihui Tang, Chuancong Luo, Chuanshang Xu, Yifan Liu, Bin Zhang, Hai-Tao School of Artificial Intelligence and Automation The Engineering Research Center of Autonomous Intelligent Unmanned Systems The Key Laboratory of Image Processing and Intelligent Control Wuhan430074 China The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China

ISBN: (纸本)9789887581543

This paper proposes a fog weather data augmentation method for the unmanned surface vessels (USVs) via improved Generative Adversarial Network(GAN) model. First, a generator scheme for GAN is proposed with the guided generation of the atmospheric scattering model in this paper. A Laplacian Pyramid Based Depth Residuals model is added to the generator which reduces the difficulty of generating fog images caused by the degradation of water surface image and improves the quality of generated images. Finally, fog images are generated from sunny weather images collected with HUST-12C by LPBDR-GAN model and experiments show that generated images are very close to real fog images. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Generative adversarial networks

来源：评论

学校读者我要写书评

暂无评论

Residual Life Prediction of Rolling Bearings Based on Transformer-BiGRU-Attention Model with Improved Sparrow Optimization Algorithm 1

引用

TEPEN International Workshop on Fault Diagnostics and Prognostics, TEPEN-IWFDP 2024

作者： An, Xunmeng Zhang, Chao Liu, Caiye Liu, Guiyi Hao, Jinzhang College of Mechanical Engineering Inner Mongolia University of Science and Technology Baotou014010 China Inner Mongolia Autonomous Region Key Laboratory of Intelligent Diagnosis and Control of Electromechanical Systems Baotou014010 China

ISBN: (数字)9783031694837

ISBN: (纸本)9783031694820

Remaining useful life (RUL) prediction is one of the core problems of PHM, and the traditional deep learning method has low accuracy in predicting the remaining useful life of rolling bearings. A hybrid deep learning model of Transformer-BiGRU-Attention based on improved sparrow optimization algorithm is proposed for rolling bearing remaining useful life prediction. The current sparrow optimization algorithm has the problems of slow convergence, lack of robustness and falling into local optimum. Therefore, an enhanced version of the sparrow search algorithm, termed the Chaotic Mapping-Sparrow Search Algorithm (CSSA), is introduced. The utilization of chaotic cube mapping for initializing the population enhances the quality and diversity of distribution within the population. This approach effectively prevents the algorithm from getting trapped in local optima and optimizes both the speed of convergence and the accuracy of the solutions. Tests on RUL prediction of rolling bearings show that the proposed method can more accurately achieve the RUL prediction of bearings under variable operating conditions, and the accuracy of RUL prediction results is higher than that of current methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Mapping

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：