主题: Graph Signal Processing is Combined with Deep Learning for Security Detection of Wind Turbine Blades
报告人：浙江大学，潘翔，教授
报告时间：2023年12月23日（周六）下午14:00
报告地点：李文正楼4楼会议室
主办单位：东南大学网络空间安全学院
承办单位：江苏省网络空间安全学会
报告简介：
For early warning the damaged blade of wind turbines, an emission noise processing framework is proposed based on combination of Graph signal processing and Deep Learning. A microphone array is utilized to receive the noise emitted by the wind turbine blades. The weak abnormal signal from the damaged blade is enhanced by beamforming techniques. The enhanced signal is transformed into the graph domain by Graph Fourier Transform, from which the Mel filter bank features are extracted as inputs of a Multi-scale Feature Aggregation Conformer (MFA-Conformer) for damage detection. The MFA-Conformer combines Transformers and convolution neural networks (CNNs) to capture global and local features from the frequency or Graph domain. And the multi-stage aggregation strategy is utilized to exploit hierarchical context information. The reduction in the computational cost is achieved in the CNNs-based damage detection due to the real-valued features extracted from graph domain. The MFA-Conformer neural network is trained on the dataset which is created by applying data augmentation to the training samples. With the Mel filter bank features extracted from the frequency and graph domains, the MFA-Conformer neural network performs well in the five wind-farm data tests, with 2.55 % improvement in accuracy over the residual networks.
个人简介：
潘翔，浙江大学教授、博士生导师。2003年获得浙江大学信息与通信工程专业博士学位。2011年4月~2012年4月加拿大Victoria 大学访问学者。2018年5月~9月美国Connecticut 大学访问学者。主讲本科生必修课程“数字信号处理”和研究生学位课程“现代信号处理”。主要研究方向：统计与自适应信号处理、声信号处理、模式识别和图像处理。主持包括六项国家自然科学基金在内的二十多项国家级科研项目。发表SCI、EI学术论文100多篇，已授权国家发明专利十四项。获得2019年浙江省科技进步二等奖1项。担任浙江省信号处理学会理事兼任副秘书长。兼任J. Acoust. Soc. Am.、Applied Acoustics、Signal Process.等杂志特约审稿人。
