2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)最新文献_第8页

Research on Secondary Electron Emission Characteristics of Metal Materials based on Monte-Carlo Simulation 基于蒙特卡罗模拟的金属材料二次电子发射特性研究

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058447

Siyu Tian, Yu Chen, Zecai Chen, Long Chen, C. Zhang, Sichao Zhang, Xiaohan Hou, Shuang Wang, Yonghong Cheng

{"title":"Research on Secondary Electron Emission Characteristics of Metal Materials based on Monte-Carlo Simulation","authors":"Siyu Tian, Yu Chen, Zecai Chen, Long Chen, C. Zhang, Sichao Zhang, Xiaohan Hou, Shuang Wang, Yonghong Cheng","doi":"10.1109/ICSMD57530.2022.10058447","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058447","url":null,"abstract":"With the rapid development of astronautics, various types of spacecrafts have been widely used. However, the secondary electron multiplication effect can affect the stability of the spacecraft. To solve this problem, it is necessary to study the secondary electron emission characteristics of metal materials. In this paper, the emission characteristics of secondary electrons, and the physical process of secondary electron radiation are introduced, and an elastic scattering cross-section model represented by mott cross-section and a model based on Penn dielectric function theory is established. The Monte Carlo method is the most common method in this field, on the basis of drawing a diagram, the effect of the incidence angle, sample material, and other factors on the secondary electron emission yield are analyzed, and finally, the correctness of the Monte Carlo method in this paper is verified by the comparison of simulation data and experimental data.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"72 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":"129867814","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

Salient Object Detection Based on Unified Graph Neural Network Joint Learning 基于统一图神经网络联合学习的显著目标检测

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058426

Tiantian Wang, Yunbo Hu, Zheng Yan, Jiaqing Qiao, Bing Liu

{"title":"Salient Object Detection Based on Unified Graph Neural Network Joint Learning","authors":"Tiantian Wang, Yunbo Hu, Zheng Yan, Jiaqing Qiao, Bing Liu","doi":"10.1109/ICSMD57530.2022.10058426","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058426","url":null,"abstract":"In complex visual scene, the performance of existing deep convolutional neural network based methods of salient object detection still suffer from the loss of high-frequency visual information and global structure information of the object, which can be attributed to the weakness of convolutional neural network in capability of learning from the data in non-Euclidean space. To solve these problems, an end-to-end unified graph neural network joint learning framework is proposed, which realizes the joint learning process of salient edge features and salient region features. In this learning framework, we construct a multi-relations dynamic attention graph convolution operator, which captures non-Euclidean space global context structure information by enhancing message transfer between different graph nodes. Further, by introducing a graph attention fusion module, the full use of salient edge cues and salient region cues is achieved. Finally, by explicitly encoding the salient edge information to guide the feature learning of salient regions, salient regions in complex scenes can be located more accurately. The experiments on three public benchmark datasets show that our method has competitive detection results compared with the current mainstream deep convolutional neural network based salient object detection methods. More importantly, it uses fewer parameters and less computation, so it is a lightweight salient object detection model.","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":"133408467","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 and Diagnosis Method Based on Multi-Channel Feature Fusion Residual Network 基于多通道特征融合残差网络的故障特征提取与诊断方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058295

Hu Yu, Xiaodong Miao, Fan Ping, Zhiwen Xun, Yinji Gu

引用次数: 2

Deployable and Self Adaptive System of Intelligent Thermal Control of a Micro Satellite 微卫星智能热控可展开自适应系统

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058395

Liu Liu, Xiaofeng Zhang, Hong-guang Liang, Kangli Bao, Xiaofei Zhu

{"title":"Deployable and Self Adaptive System of Intelligent Thermal Control of a Micro Satellite","authors":"Liu Liu, Xiaofeng Zhang, Hong-guang Liang, Kangli Bao, Xiaofei Zhu","doi":"10.1109/ICSMD57530.2022.10058395","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058395","url":null,"abstract":"At present, the periodic power of equipment on micro satellite has reached hundreds of watts, and the temperature stability of equipment is required in a specific small range in complex variable thermal environment. Due to the main limitations of micro satellite heat dissipation capacity, energy, space, the traditional thermal control system can not meet the requirement. Therefore, a more efficient and intelligent system was proposed. In this paper, the system composition, deployable radiator, flexible loop heat pipe (LHP), data analysis of sensing and measuring, intelligent temperature control algorithm, simulation analysis and test verification were introduced and expounded. The system solved the heat dissipation and precise temperature control of periodic high power equipment in variable environment which has the characteristics of deployable and self adaptive. Compared with the traditional system, this system consumes less thermal compensation in non-working time, also be lighter in weight and smaller in volume, which makes it very suitable for micro satellites.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"19 2 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":"116681083","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 Diagnosis of Civil Aircraft Avionics System Based on Bayesian Network and Function Ground Test 基于贝叶斯网络和功能地面试验的民机航电系统故障诊断

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058309

Yanchun Fan, Junyi Zhou, Xuwei Yang, Zeming Xie, D. Tang

引用次数: 0

An online detection method for capacitor voltage transformer based on load classification 基于负载分类的电容式电压互感器在线检测方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058214

Yuxuan Zhang, Chuanji Zhang, Hongbin Li, Qing Chen, Cheng Cheng, Panpan Guo

{"title":"An online detection method for capacitor voltage transformer based on load classification","authors":"Yuxuan Zhang, Chuanji Zhang, Hongbin Li, Qing Chen, Cheng Cheng, Panpan Guo","doi":"10.1109/ICSMD57530.2022.10058214","DOIUrl":"https://doi.org/10.1109/ICSMD57530.2022.10058214","url":null,"abstract":"capacitor voltage transformers (CVT) are widely used in the power system due to their good insulation and low cost. An accurate measurement performance provides critical information to ensure the safe and efficient operation of the power system. Therefore, online measurement error detection has received extensive attention. However, the problem of detecting CVTs deployed in substations with fluctuated loads has not been solved, because the frequent switching of these loads changes the size of monitoring indicators, resulting in misjudgment. In this paper, an online detection method based on load classification has been proposed. Several identification parameters are first put forward to classify loads. With these parameters, low-load modeling and monitoring data of the period of industrial users stably out of operation are screened out. Finally, online monitoring is achieved by the principal component analysis method. The efficacy of this method is verified in a 220kV substation actuating a steel plant.","PeriodicalId":396735,"journal":{"name":"2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD)","volume":"29 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":"134219748","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

Optimization design and experimental verification of flywheel bracket for micro satellites 微型卫星飞轮支架的优化设计与实验验证

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058363

Yanyan Zhao, Jianjun Jia, Yang Liu

引用次数: 0

Power Transmission Line Defect Recognition Method Based on Binocular Feature Fusion and Improved FCOS Detection Head 基于双目特征融合和改进FCOS检测头的输电线路缺陷识别方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058247

Ming Mao, Lu Liu, Wenxiang Chen, Weiqi Xiong, Xuelei Xi, Guoqiang Zhu, Yan Zhang, Shuang Wang, Yu Chen

引用次数: 0

A Reliability Analysis Method for an Aircraft Power System with Hybrid Uncertainty and Failure Dependence 具有不确定性和故障相关性的飞机动力系统可靠性分析方法

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058438

Yufei Song, Yukui Zhu, Xiang Cao, Min Yu, Jie Zhang

引用次数: 0

Testability design of spacecraft test equipment based on BIT 基于BIT的航天器测试设备可测试性设计

2022 International Conference on Sensing, Measurement & Data Analytics in the era of Artificial Intelligence (ICSMD) Pub Date : 2022-11-30 DOI: 10.1109/ICSMD57530.2022.10058367

Yang Zhao, He Liu, Rui Li, Xiaojun Han, Chun Zhang, Naihai Li

引用次数: 0