2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)最新文献_第9页

A Robust Strategy for Roadside Cooperative Perception Based on Multi-Sensor Fusion 基于多传感器融合的道路协同感知鲁棒策略

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058282

Shaowu Zheng, Chong Xie, Shanhu Yu, Ming Ye, Ruyi Huang, Weihua Li

{"title":"A Robust Strategy for Roadside Cooperative Perception Based on Multi-Sensor Fusion","authors":"Shaowu Zheng, Chong Xie, Shanhu Yu, Ming Ye, Ruyi Huang, Weihua Li","doi":"10.1109/ICSMD57530.2022.10058282","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058282","url":null,"abstract":"Roadside perception is a fundamental task for vehicle-to-road cooperative perception and traffic scheduling. However, most existing roadside perception strategies prefer to deploy sensors in a single perspective or test in a simulation environment. Due to the limited field of view covered by a single sensor, such methods usually cannot continuously detect the same object from different viewpoints or provide a wide sensing range in complex scenarios. To address these issues, a robust strategy for roadside cooperative perception based on multi-sensor fusion (RCP-MSF) is proposed in this paper. A 2D object detector is improved based on the NanoDet model to handle multiple images simultaneously. In addition, an ultra-fast 3D object detection strategy is suggested based on point cloud processing rather than relying on existing high-cost deep-learning models. Moreover, to match the 2D and 3D bounding boxes, a data association module for multi-modal sensor information fusion is presented. Any 2D and 3D object detector can follow this module. Furthermore, a roadside perception dataset named SCUT-V2R is constructed to verify the performance of the proposed method. Experiments on the dataset demonstrate that the RCP-MSF outperforms the camera-only and lidar-only strategies in object detection precision while maintaining real-time performance.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130167308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Sinc-based Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis under Small Samples 基于自适应卷积网络的小样本设备智能边缘诊断

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058219

Rui Liu, Xiaoxi Ding, Qihang Wu, Hao Xiang, H. Tan, Y. Shao

{"title":"Sinc-based Multiplication-Convolution Network for Equipment Intelligent Edge Diagnosis under Small Samples","authors":"Rui Liu, Xiaoxi Ding, Qihang Wu, Hao Xiang, H. Tan, Y. Shao","doi":"10.1109/ICSMD57530.2022.10058219","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058219","url":null,"abstract":"Data-driven intelligent diagnosis models need massive monitoring data to train themselves for desired performance. However, in many engineering scenarios, collecting fault data is often expensive and time-consuming, which leads to few-shot learning becoming a valuable research hotspot for intelligent diagnosis. Inspired by mode characteristics and feature enhancement learning, this study propose a Sinc-based multiplication-convolution network (SincMCN) for intelligent fault diagnosis under small samples. It works in frequency domain, and consists of only three layers, including a feature separator, a feature extractor and a classifier. In the feature separator, a series of Sinc-based multiplication filtering kernels (SincMFKs) are designed for improving the utilization of fault information of spectrum samples. The products between SincMFKs and spectrum samples are stacked into activated mode spectrum images (AMSIs) with rich fault-related features retained. Since AMSIs are concise enough, this study employs only a 2D convolutional layer and a fully connected layer as the feature extractor and the classifier for achieving a fast and precise pattern recognition. Experimental results show SincMCN has better diagnosis accuracy and stronger potentials for few-shot diagnosis compared other cutting-edge models. Specially, analytic filtering kernels not only cut down the model parameters for edge diagnosis and provide powerful application potentials and engineering value for online monitoring of rotating machinery.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"86 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130474836","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Fault Diagnosis Methods for Running Gears of Urban Rail Trains 城市轨道车辆运行齿轮故障诊断方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058411

W. Ye, Minghao Wu, Lifeng Wu

引用次数: 0

A Glass Curtain Wall Bolt Loosening Monitoring using Piezoelectric Impedance Measurement and 1D-CNN-based Transfer Learning 基于压电阻抗测量和一维cnn迁移学习的玻璃幕墙螺栓松动监测

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058270

Zhuo Chen, Jiawen Xu, Ruqiang Yan

{"title":"A Glass Curtain Wall Bolt Loosening Monitoring using Piezoelectric Impedance Measurement and 1D-CNN-based Transfer Learning","authors":"Zhuo Chen, Jiawen Xu, Ruqiang Yan","doi":"10.1109/ICSMD57530.2022.10058270","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058270","url":null,"abstract":"The safety of a glass curtain wall is depending on the tightness of its bolts. Traditional bolt loosening monitoring often uses a number of simple and crude methods to obtain data, such as the hammering method, where the target structure is hammered. Then the prediction is performed using conventional signal processing methods. Such methods tend to be of limited accuracy, time-consuming and laborious, highly dependent on individual experience, and of poor generality. In this research, we introduce the electromechanical impedance method and adopt transfer learning for the structural health monitoring of the glass curtain wall. The impedance of the structures with tight and loose bolts is measured by dual-piezoelectric transducers. The inductance shunt circuit is integrated for enriching the data of the source domain. The features of the structures in the source domain are extracted using a one-dimensional convolutional neural network and then transferred to a target domain. A fine-tuning method is used to improve the accuracy of the monitoring of bolt looseness due to a small sample of the target domain. From what has been discussed above, the proposed method combines EMI and transfer learning, which is convenient to operate, avoids too much human intervention, and has good accuracy and versatility. Experimental analysis proves the effectiveness of the proposed method in the health monitoring of glass curtain walls.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"10 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128894545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Soil Type Recognition for Robotic Sampling in Deep Space Exploration using Haptic Information 基于触觉信息的深空探测机器人采样土壤类型识别

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058350

Xinkai Wang, Fengyuan Liu, Lifeng Zhu, Ai-Guon Song

引用次数: 0

Fault Diagnosis Method of Rolling Bearings Under Different Working Conditions Based on Federated Feature Transfer Learning 基于联邦特征迁移学习的滚动轴承不同工况故障诊断方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058221

Shouqiang Kang, Jiawei Yang, Yulin Sun, Yujing Wang, Qingyan Wang, V. I. Mikulovich

{"title":"Fault Diagnosis Method of Rolling Bearings Under Different Working Conditions Based on Federated Feature Transfer Learning","authors":"Shouqiang Kang, Jiawei Yang, Yulin Sun, Yujing Wang, Qingyan Wang, V. I. Mikulovich","doi":"10.1109/ICSMD57530.2022.10058221","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058221","url":null,"abstract":"A rolling bearing fault diagnosis method based on the federated feature transfer learning is proposed for the low accuracy of the diagnosis model in the presence of large differences in data distribution under different working conditions, difficulty in obtaining labeled data and non-sharing of data among different users. This method performs wavelet transformation on the time domain vibration data of rolling bearings to obtain a time-frequency diagram. The priori labeled public data and the multi-user island private data are regarded as the source domain and the target domain. The multi-representation feature extraction structure is introduced to improve the original residual network. Based on an improved residual network and multi-representation features in the source domain and the target domain, every local model and a federated global model are constructed. Through verification of bearing data, the proposed method can establish an effective fault diagnosis model with high fault diagnosis accuracy. It can integrate the knowledge of isolated island data without sharing data among multiple users.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117281155","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

ATPRINPM: A single-source domain generalization method for the remaining useful life prediction of unknown bearings ATPRINPM:一种用于未知轴承剩余使用寿命预测的单源域泛化方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058424

Juan Xu, Bin Ma, Yuqi Fan, Xu Ding

{"title":"ATPRINPM: A single-source domain generalization method for the remaining useful life prediction of unknown bearings","authors":"Juan Xu, Bin Ma, Yuqi Fan, Xu Ding","doi":"10.1109/ICSMD57530.2022.10058424","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058424","url":null,"abstract":"The remaining useful life (RUL) of bearings is critical to the proper operation of mechanical equipment, maintenance of equipment costs and availability. The existing domain adaptation methods have had great success in RUL prediction. However, when the target bearing data are unavailable or unknown to be involved in model training, the domain adaptation approaches also incapable. To solve the problem, we propose a parallel reversible instance normalization method based on adaptive threshold stage division for remaining useful life prediction of unknown bearings. First, we design an adaptive threshold method to find degradation points to divide the healthy and degradation stages. Then according to time series, we merge the original vibration data and its instance normalized data to increase the data distribution diversity. Finally, we combine instance normalization and parallel reversible normalization of the source bearing data into unified RUL learning framework to solve the uncertainty of counterfactual data and improve RUL prediction performance. The results show that the method is superior to the state-of-the-art methods for RUL prediction of unknown bearings.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"38 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115467317","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Fault feature extraction of rolling bearing considering slippage influence based on a dynamic model 基于动态模型考虑滑动影响的滚动轴承故障特征提取

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058450

Zheng Cao, Ziqin Kang, Yongbin Liu, Zhongding Fan, Jie Chen, Xianzeng Liu

{"title":"Fault feature extraction of rolling bearing considering slippage influence based on a dynamic model","authors":"Zheng Cao, Ziqin Kang, Yongbin Liu, Zhongding Fan, Jie Chen, Xianzeng Liu","doi":"10.1109/ICSMD57530.2022.10058450","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058450","url":null,"abstract":"Bearing spalling, pitting and other local faults are one of the common bearing faults, which are quite difficult to detect in the early stage. Fault characteristic frequency is the most widely used in fault diagnosis. However, bearings are likely to slip during operation, which will result in the deviation between theoretical and actual fault characteristic frequencies. This paper proposes a dynamic model of a defective rolling bearing considering slippage to evaluate the fault characteristic frequency. The interactions among inner ring, rolling body, cage, and outer ring, as well as the time-varying displacement excitation of the outer raceway spalling are considered in the constructed dynamic model. The effects of slippage on the fault characteristic frequency at different speeds and loads are investigated using the proposed dynamic model, and an experiment was conducted to validate the proposed model. The results show that the actual fault characteristic frequency will be smaller than the theoretical fault characteristic frequency at high speed and light load. The proposed model provides a new method for modelling bearing dynamics and a theoretical basis for monitoring and diagnosing bearing faults.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131514404","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Effect of RISC Positions on End Winding Vibrations in Synchronous Generators RISC位置对同步发电机端部绕组振动的影响

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058454

Yu‐Ling He, Ming‐Xing Xu, Wen Zhang, De‐Rui Dai, Xiaolong Wang, Xiang-Ao Liu, Wen-Jie Zheng, Yong Li, D. Gerada

引用次数: 0

Research on credibility evaluation method of infrared small target detection network based on YOLOv5 基于YOLOv5的红外小目标探测网络可信度评估方法研究

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058215

Chang'an Wei, Dandan Song, B. Li, Qiqi Li, Shouda Jiang

引用次数: 0