卢森骧 - 东北大学 - 信息科学与工程学院

个人简介

教育经历 2007.9-2011.7 东北大学信息科学与工程学院测控技术与仪器专业学士学位 2011.9-2013.7 东北大学信息科学与工程学院电气工程专业硕士学位 2013.9-2018.7东北大学信息科学与工程学院控制理论与控制工程专业博士学位工作经历 2018.7至今东北大学计算机科学与工程学院博士后 2018.7至今东北大学信息科学与工程学院讲师招收博士/硕士方向欢迎电气工程专业学生报考硕士研究生。学术成果专著或教材 [1]基于漏磁内检测器的管道缺陷数据处理方法，科学出版社，2016 专利 [5]基于改进粒子群算法的不规则管道缺陷的反演方法，201910881235.9 [6]一种基于LS-KNN的管道漏磁内检测缺失数据插补方法，201811451849.5 [7]一种SVM有向无环图的海底管道风险评估方法，201910589274.1 [8]一种基于步进环栅的输电线路的多目标优化路径选择方法，201910834937.1 [9]一种管道内检测漏磁数据智能分析系统及方法，201811633698.5 [10]一种管道漏磁数据的边界精确识别方法，201910788496.6 [11]一种快速管道漏磁数据多尺度异常区域推荐系统及方法，201910387892.8 [12]一种管道漏磁数据的高清可视化方法，201910522202.5 联系方式办公室：信息楼102

研究领域

研究方向电气自动化；人工智能；故障诊断

近期论文

查看导师最新文章（温馨提示：请注意重名现象，建议点开原文通过作者单位确认）

期刊论文 [1]An estimation method of defect size from MFL image using visual transformation convolutional neural network[J]. IEEE Transactions on Industrial Informatics, 2019, 15(1): 213-224. [2]Precise inversion for the reconstruction of arbitrary defect profiles considering velocity effect in magnetic flux leakage testing[J], IEEE Transactions on Magnetics, 2017, 53(4): Article Sequence Number 6201012. [3]A sensor liftoff modification method of magnetic flux leakage signal for defect profile estimation[J], IEEE Transactions on Magnetics, 2017, 53(7): Article Sequence Number 6201813. [4]Domain knowledge-based deep-broad learning framework for fault diagnosis[J], IEEE Transactions on Industrial Electronics, 2020, In Press, DOI:10.1109/TIE.2020.2982085. [5]Injurious or noninjurious defect identification from MFL images in pipeline inspection using convolutional neural network[J], IEEE Transactions on Instrumentation and Measurement, 2017, 66(7): 1883-1892. [6]Fast reconstruction of defect profiles from magnetic flux leakage measurements using an RBFNN based error adjustment methodology[J], IET Science, Measurement & Technology, 2017, 11(3): 262-269. [7]Stability analysis and stabilization for fuzzy hyperbolic time-delay system based on delay partitioning approach[J], Neurocomputing, 2016, 214: 555-566. [8]Feng Jian, Zhang Junfeng, Lu Senxiang, Wang Hongyang, Ma Ruize. Three-axis magnetic flux leakage in-line inspection simulation based on finite-element analysis[J], CHINESE PHYSICS B, 2013, 22(1): 01810301-01810306. [9]Anomaly detection of complex MFL measurements using low-rank recovery in pipeline transportation inspection[J], IEEE Transactions on Instrumentation and Measurement. 2020, In Press, DOI:10.1109/TIM.2020.2974543. [10]Quick reconstruction of arbitrary pipeline defect profiles from MFL signals employing modified harmony search algorithm[J], IEEE Transactions on Instrumentation and Measurement, 2018, 67(9): 2200-2213. 会议论文 [2]A time weight convolutional neural network for positioning internal detector[C]. 2019 IEEE 31st Chinese Control and Decision Conference (CCDC), Nanchang, China, 2019:4666-4669. [3]Extracting defect signal from the MFL signal of seamless pipeline[C]. 2017 IEEE 29th Chinese Control and Decision Conference (CCDC), Chongqing, China, 2017:5209-5212. [4]Convolution neural network for classification of magnetic flux leakage response segments[C]. 2017 IEEE 7th Data Driven Control and Learning Systems (DDCLS), Chongqing, China, 2017:152-155.