庞少鹏

2018年毕业于北京航空航天大学获博士学位，2018年7月至今工作于齐鲁工业大学信息与自动化学院。

研究领域

复杂网络控制；深度学习；机器视觉

主讲课程

《MATLAB及电力系统仿真》

论文(*表示通讯作者)

[1] Shao-peng Pang*, Zhi Tian, Wei-gang Ma.Structural controllability and observability of complex network with output feedback.Physica A: Statistical Mechanics and its Applications, 2023,620: 128790. [SCI收录]

[2] Shao-peng Pang*, Yue Zhou, Xiang Ren,Fang-zhou Xu.Structural controllability of general edge dynamics in complex network.Scientific Reports, 2023,13:3393. [SCI收录]

[3]Chuan-zheZhang, Jia-hao Li, Shao-peng Pang*,Shu-wang Zhou. A 12-lead ECG correlation network model exploring the inter-lead relationships. Europhysics Letters, 2022, 140(3): 31001.[SCI收录]

[4] Jia-hao Li, Shao-peng Pang*, Fang-zhou Xu, Peng Ji, Shu-wang Zhou, Ming-lei Shu*. Two-dimensional ECG-based cardiac arrhythmia classification using DSE-ResNet. Scientific Reports, 2022, 12(1): 1-13. [SCI收录]

[5] Fang-zhou Xu*, Xiao-yan Xu, Ya-nan Sun, Jin-cheng Li, Ge-ge Dong, Yuan-dong Wang, Han Li, Lei Wang, Ying-chun Zhang, Shao-peng Pang*, SenYin*. A framework for motor imagery with LSTM neural network. Computer methods and programs in biomedicine, 2022, 218: 106692. [SCI收录]

[6] Chuan-zhe Zhang, Shao-peng Pang*, Hao Yu, Guo-zheng Han. A fund-stock network projection model. Physica A: Statistical Mechanics and its Applications, 2021, 566: 125630. [SCI收录]

[7] Shu-lin Liu, Shao-peng Pang*. Effect of Degree Correlation on Edge Controllability of Real Networks. Chinese Physics B, 2020, 29(10): 100202. [SCI收录]

[8] Chao Li, Shao-peng Pang*. The Role of Individual Edges in Edge Controllability of Complex Networks. IEEE Access, 2020, 8: 63559-63566. [SCI收录]

[9] Shao-peng Pang*, Wen-xu Wang, Fei Hao. Controllability limit of edge dynamics in complex networks. Physical Review E, 2019, 100(2): 022318. [SCI收录]

[10] Shao-peng Pang*, Chao Li, Cong Fang, Guo-zheng Han. Controlling edge dynamics in multilayer networks. Physica A: Statistical Mechanics and its Applications, 2019, 528: 121273. [SCI收录]

[11] Shao-peng Pang, Fei Hao*. Effect of interaction strength on robustness of controlling edge dynamics in complex networks. Physica A: Statistical Mechanics and its Applications, 2018, 497: 246-257. [SCI收录]

[12] Shao-peng Pang, Fei Hao*. Target control of edge dynamics in complex networks. Physica A: Statistical Mechanics and its Applications, 2018, 512: 14-26. [SCI收录]

[13] Shao-peng Pang, Fei Hao*, Wen-xu Wang*, Ying-cheng Lai. Universal framework for edge controllability of complex networks. Scientific Reports, 2017, 7(1): 4224. [SCI收录]

[14] Shao-peng Pang, Fei Hao*. Optimizing Controllability of Edge Dynamics in Complex Networks by Perturbing Network Structure. Physica A Statistical Mechanics & Its Applications, 2017, 470: 217-227. [SCI收录]

[15] Shao-peng Pang, Fei Hao*. Controllable subspace of edge dynamics in complex networks. Physica A Statistical Mechanics & Its Applications, 2017, 481: 209-223. [SCI收录]

[16] Shao-peng Pang*, Fei Hao*, Wen-xu Wang*. Robustness of controlling edge dynamics in complex networks against node failure. Physical review E, 2016, 94(5): 052310. [SCI收录]

[17] Shi-ming Chen, Shao-peng Pang*, Xiao-qun Zou. An LCOR model for suppressing cascading failure in weighted complex networks. Chinese Physics B, 2013, 22(5): 058901. [SCI收录]

[18] Zhi-liangAn, Shao-peng Pang*, Ming-jun Du, Peng Ji.Observability of Edge Dynamics in Complex Networks.Chinese Intelligent Systems Conference. Singapore: Springer Nature Singapore, 2023: 407-414.[EI收录]

[19]Tian-yuWang,Shao-peng Pang*.Robustness ofcontrollingswitchednetworksagainstedgefailure.42nd Chinese Control Conference. IEEE, 2023: 2207-2212.[EI收录]

[20]ZhiTian, Shao-peng Pang*,PengJi, Wei-gang Ma. Robustness ofcontrolling andobservingedgedynamics incomplexnetworks.3rd International Conference on Computer Engineering and Intelligent Control. 2022: 66-72.[EI收录]

[21] Shao-peng Pang, Fei Hao*. Driver edges analysis for network controllability.34th Chinese Control Conference. IEEE, 2015: 7291-7296.[EI收录]

[22]陈世明,庞少鹏,邹小群,方华京,陈曜.面向级联失效的加权网络负载容量非线性模型鲁棒性优化.控制与决策, 2013, 28(7): 1041-1045.[EI收录]

专利

[1]庞少鹏,王可,武其龙.一种绕线画自动算法, ZL20200540631.8

[2]庞少鹏,李家豪,舒明雷.一种基于十二导联心电数据二维化的多输入残差神经网络的ECG信号分类方法,ZL20210784950.8

[3]庞少鹏,李家豪,李广良.一种基于Opencv的菲涅尔透镜中心定位方法, ZL202210083989.1

[4]李广良,庞少鹏,钟成卫,李正平.一种基于Deformable-DAB-DETR的高透光菲涅尔透镜实时检测方法, ZL202311453576.9

[5]庞少鹏,李广良,崔涛.一种双操作台的菲涅尔透镜激光切割机, ZL202310015015.4

[6]庞少鹏,李广良,崔涛.一种菲涅尔透镜加工设备及其使用方法, ZL202311609407.X

科研项目

[1]国家自然科学基金青年基金项目，61903208，时序网络边动态系统的可控性研究，2020/01-2022/12，23万元，结题，项目负责人。

[2]齐鲁工业大学（山东省科学院）青年博士合作基金项目，2019BSHZ0014，基于图卷积神经网络的心电图识别方法研究，2020/01-2021/12，9.6万元，结题，项目负责人。

[3] 2021年山东省科技型中小企业创新能力提升工程项目，太阳能聚光光伏透镜自动化生产线项目，2021/12-2023/12，50万元，结题，第二单位负责人。

[4]菲涅尔透镜自动化生产线项目，2022/01-2023/12，20万元，结题，项目负责人。

[5] 2022年度齐鲁工业大学（山东省科学院）科教产融合试点培优基金，2022/01-2023/12，30万元，结题，项目负责人。

[6]山东省自然科学基金面上项目，ZR2021MF022，基于惩罚似然与神经网络的复杂化工过程动态软测量算法研究，2022/01-2024/12，10万元，在研，第二位参与人。

[7]山东省高等学校青年创新科技支持计划，2021KJ025，地空机器人视觉伺服协作控制关键技术研究，2022/01-2024/12，50万元，在研，第五位参与人。

[8]济南市“新高校20条”资助项目，2021GXRC07，基于脑机接口的脊髓损伤临床康复机制研究，2022/01-2024/12，120万元，在研，第四位参与人。

[9]国家重点研发计划，2023YFF1203900，高精度生物感知觉反馈操纵技术与系统，2023/10-2026/10，45万元，在研，第二单位第三位参与人。

获奖

[1] 2018年北京市优秀博士研究生毕业生。

[2] 2022年常州市“龙城英才计划”（创业类）。

[3]2023年指导学生获二十五届中国机器人及人工智能大赛全国总决赛一等奖。