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  • 哈尔滨工业大学机电工程学院
  • 华中科技大学机械科学与工程
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硕士生常元洪参加国际会议回国报告

发布时间:2019-12-31 点击数:


汇报题目:ACMAE 2019 参会报告

汇报时间:202012(星期四) 16:00

汇报地点:创新港 2号巨构 2149会议室

汇报人:常元洪

会议名称:2019 The 10th Asia Conference on Mechanical and Aerospace Engineer, ACMAE 2019

会议时间:26-28 December, 2019

会议地点Bangkok, Thailand

会议概况:The major goal and feature of ACMAE 2019 is to bring academic scientists, engineers, industry researchers together to exchange and share their experiences and research results, and discuss the practical challenges encountered and the solutions adopted. Prestigious experts and professors have been invited to deliver the latest information in their respective expertise areas. The conference has 2 Keynote Speakers, 1 Plenary Speaker and 3 Technical Sessions. It will be a golden opportunity for the students, researchers and engineers to interact with the experts and specialists to get their advice or consultation on technical matters, sales and marketing strategies.

参加论文信息

Title: Intelligent Fault Diagnosis of Satellite Communication Antenna via a Novel Meta-learning Network Combining with Attention Mechanism

Author: Yuanhong Chang, Jinglong Chen, Shuilong He

Abstract: Shipborne satellite communication antenna which is used for remote control plays an irreplaceable role in ships, it is necessary to monitor its operation state. However, obtaining sufficient fault information in mass monitoring data is particularly difficult, which greatly degrades performance of existing intelligent algorithms. In this paper, a novel meta-learning network is proposed to realize state recognition of shipborne antenna under small samples prerequisite. The network is constructed to improve generalization even though inputs collected under different operating conditions. Meta-learning network consists of sampler, feature extractor, auxiliary classifier and discriminator. It trains an adaptive pseudo-distance to evaluate the degree of correlation between different data, then realize classification task. Feasibility and effectiveness of the network are verified by three bearing datasets. Results show that the proposed method uses few samples to successfully classify mechanical data of shipborne antenna even with different rotating speed and random noise.


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