Presentation on returning from the international conference by master student Yuanhong CHANG
Topic：ACMAE 2019 conference presentation
Time：Thursday 16:00pm, January 2, 2020
Location：Room 2149, Building 2, iHarbour campus
Presenter：Yuanhong CHANG (常元洪)
Conference Name：2019 The 10th Asia Conference on Mechanical and Aerospace Engineer, ACMAE 2019
Conference Time：26-28 December, 2019
Conference Location：Bangkok, Thailand
Conference introduction：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.
Information of conference paper
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.
