讲师

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

研究方向

电气自动化；人工智能；故障诊断

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。

项目

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

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.

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

邮箱：lusenxiang@ise.neu.edu.cn