主要学术成果

学术论文：

  1. Jiang H, Yang D, Zhi Z, et al. A normal weld recognition method for time-of-flight diffraction detection based on generative adversarial network[J]. Journal of Intelligent Manufacturing, 2022: 1-17. ( WOS: 000532091500001,影响因子：8.3)
  2. Yang D, Jiang H*, Liu Z, et al. Radiographic image enhancement based on a triple constraint U-Net network[J]. Insight-Non-Destructive Testing and Condition Monitoring, 2022, 64(9): 511-519. (WOS:000373565000005, 影响因子: 1.1)
  3. Zhi Z, Jiang H*, Yang D, et al. An end-to-end welding defect detection approach based on titanium alloy time-of-flight diffraction images[J]. Journal of Intelligent Manufacturing, 2023, 34(4): 1895-1909.  ( WOS: 000532091500001,影响因子：8.3)
  4. Cheng Z, Jiang H*, Jing Q, et al. TOFD Image Enhancement Method of Titanium Alloy Welds Based on Anisotropic Diffusion Model with Regional Adaptive Strategy[C]. 2022 IEEE 6th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC). IEEE, 2022: 1869-1874.(EI:20224713149198)
  5. Cheng H, Jiang H*, Liu Z, et al. Radiographic Image Enhancement Method For Complex Components based on Deep Learning Theory[C]. 2022 IEEE 6th Information Technology and Mechatronics Engineering Conference (ITOEC). IEEE, 2022, 6: 913-918.(EI:20221511946788)
  6. 支泽林，姜洪权，杨得焱，程志翔，高建民，王泉生，王晓桥，王景人，石养鑫. 图谱数据深度学习融合模型及焊缝缺陷识别方法[J]. 西安交通大学学报. 2021,55(05):73-82. (EI：20212110403471)
  7. Jiang HQ, Wang PX, Gao JM, Chen HY, Yang H, Shi ZG, Yang DY et al. A Method for Detecting Micro-scale Weld Defects in Complex Background[C]// Proceedings of 2021 IEEE 4th Advanced Information Management, Communicates, Electronic and Automation Control Conference (IMCEC). June 18-20, 2021.Chongqing, China. (EI:20214010976522)
  8. Hongquan Jiang*, Deyan Yang, Qihang Hu, Zelin Zhi, Jianmin Gao, Xiaoqiao Wang, Xiaoming Zhang, Huaxiang Pu, Hua Li. Sensitivity Evaluation of Radiographs Based on Multi-dimensional Data Coupling Analysis. 2020 IEEE 6th International Conference on Computer and Communications. December 11-14, 2020.Chengdu, China. (EI:20210910006414)
  9. Lu Yang*, Hongquan Jiang. Weld defect classification in radiographic images using unified deep neural network with multi-level features. [J]. Journal of Intelligent Manufacturing, 2020(3). (WOS: 000532091500001, 影响因子: 8.3)
  10. 姜洪权*, 贺帅, 高建民, 王荣喜, 高智勇, 王晓桥, 夏锋社, 程雷.一种改进卷积神经网络模型的焊缝缺陷识别方法[J]. 机械工程学报, 2020, 56(08):235-242. (EI: 20202508839155)
  11. Wang R , Peng C , Gao J , Gao Z，Jiang H. A dilated convolution network-based LSTM model for multi-step prediction of chaotic time series[J]. Computational and Applied Mathematics. 2019,39(9). DOI: 10.1007/s40314-019-1006-2. (WOS:000518846800002, 影响因子：2.239)
  12. Jiang HQ; Hu QH, Zhi ZL, Gao, JM, Gao ZY; Wang RX; He S, Li H. Convolution neural network model with improved pooling strategy and feature selection for weld defect recognition[J]. WELDING IN THE WORLD.DOI: 10.1007/s40194-020-01027-6. (WOS:000590316800002. 影响因子：2.1)
  13. Lu Yang, Hongquan Jiang*. Weld defect classification in radiographic images using unified deep neural network with multi‑level features[J]. Journal of Intelligent Manufacturing. 2020(3). DOI: 10.1007/s10845-020-01581-2 ( WOS: 000532091500001,影响因子：8.3))
  14. Hongquan Jiang*, Rongxi Wang*, Zhiyong Gao, Jianmin Gao, Hongye Wang. Classification of weld defects based on the analytical hierarchy process and Dempster–Shafer evidence theory[J]. Journal of Intelligent Manufacturing, 2019, 30(4):2013-2024. (WOS:000462014200031, 影响因子: 8.3))
  15. Hongquan Jiang, Jianmin Gao, Fengshe Xia, Xiaoming Zhang, Tao Zhou, Dongcheng Liu. Fault Recognition Technology for Pipeline Systems Based on Multi-feature Fusion of Monitoring Data[C]. ieee international conference on prognostics and health management, 2019: 1-7.(EI:20194007491408)
  16. Hongquan Jiang , Hua Li, Lu Yang, Pengxing Wang, Qihang Hu, XiaoSai Wu. A fast weld region segmentation method with noise removal[C]// Proceedings of 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference, IAEAC 2019. Piscataway，NJ，USA：IEEE，2019：1492-1496．(EI:20201108285207)
  17. Zeming Liang, Jianmin Gao, Hongquan Jiang*, Xu Gao, Zhiyong Gao, Rongxi Wang. A similarity-based method for remaining useful life prediction based on operational reliability. Applied Intelligence, 2018, 48(9):2983-2995. (WOS: 000441732900027, 影响因子: 5.3)
  18. 姜洪权, 周涛, 高建民,等. 压力容器服役质量指标体系构建及评价方法研究[J]. 西安交通大学学报, 2019(6):23-28,116. DOI: 10.7652/xjtuxb201906004. (EI: 20193507370211)
  19. Li J , Jiang H , Shang A , et al. Research on associative learning mechanisms of L2 learners based on complex network theory[J]. Computer Assisted Language Learning. DOI: 10.1080/09588221.2019.1633356. (WOS：000478361700001影响因子：7.0)
  20. H. Jiang, H. Li, L. Yang, P. Wang, Q. Hu , X. Wu. A fast weld region segmentation method with noise removal[C]. 2019 IEEE 4th Advanced Information Technology, Electronic and Automation Control Conference (IAEAC), DOI: 10.1109/IAEAC47372.2019.8997563. (EI: 20201108285207)
  21. 姜洪权, 程雷, 高建民,等. 一种考虑制造单元性能的工艺参数优化设计方法[J]. 西安交通大学学报, 201852(10):80-87. DOI: 10.7652/xjtuxb201810011. (EI: 20190206361680)
  22. 梁泽明, 姜洪权, 周秉直, 等. 多参数相似性信息融合的剩余寿命预测[J]. 计算机集成制造系统, 201824(4):813-819. DOI: 10.13196/j.cims.2018.04.001. (EI: 20183905856257)
  23. Liang Z , Jiang H , Zhou B , et al. Multi-variable similarity-based information fusion method for remaining useful life prediction[J]. Computer Integrated Manufacturing Systems, 2018, 24(4):813-819. DOI: 10.13196/j.cims.2018.04.001. (EI: 20183905856257)
  24. Liang Z , Gao J , Jiang H , et al. A Degradation Degree Considered Method for Remaining Useful Life Prediction Based on Similarity[J]. Computing in Science & Engineering. DOI: 10.1109/MCSE.2018.110145829. (WOS：000460664700007影响因子：2.1)
  25. Jiang Hongquan, Zhao Yalin*, Gao Jianmin, Gao Zhiyong. Adaptive Pseudo-Color Enhancement Method of Weld Radiographic Images Based on HSI Color Space and Self-transformation of Pixels[J]. REVIEW OF SCIENTIFIC INSTRUMENTS, 2017, 88(6):065106. (WOS: 000404641300050, 影响因子: 1.6)
  26. Wang, Rongxi; Gao, Jianmin; Gao, Zhiyong; Liang, Yanjie; Jiang, Hongquan. Information transfer-based root cause tracing for information quality problems of complex electromechanical systems in process industry. Proceedings of the 22nd MIT International Conference on Information Quality, ICIQ 2017, 2017, Proceedings of the 22nd MIT International Conference on Information Quality, ICIQ 2017(EI: 20200308053997)
  27. 姜洪权, 王岗, 高建民,等. 高血压患者并发症模式的分析方法研究[J]. 中国循证医学杂志, 2017, 17(9):6. DOI: CNKI:SUN:ZZXZ.0.2017-09-018.
  28. Wang R , Gao J , Gao Z , Gao X, Jiang H , Liang Z . Interaction Analysis-Based Information Modeling of Complex Electromechanical Systems in the Processing Industry[J]. Proceedings of the Institution of Mechanical Engineers Part I Journal of Systems and Control Engineering, 2017, 231(8):638-651. DOI: 10.1177/0959651817718454. (WOS：000407506000005影响因子:1.051)
  29. Jiang H , Wang R , Gao J , et al. Evidence fusion-based framework for condition evaluation of complex electromechanical system in process industry[J]. Knowledge-Based Systems, 2017, 124(MAY15):176-187. DOI: 10.1016/j.knosys.2017.03.011. (WOS：000400229000014影响因子:8.8)
  30. Gao Z , Wang R , Jiang H , et al. Coupling Analysis-Based False Monitoring Information Identification of Production System in Process Industry[J]. Science China Technological Sciences, 2017, 60(6):807-817. DOI: 10.1007/s11431-016-9032-7. (WOS：000402893000001影响因子：1.719)
  31. R Wang，J Gao，Z Gao，X Gao，H Jiang. Analysis of multifractality of multivariable coupling relationship of complex electromechanical system in process industry[J]. ARCHIVE Proceedings of the Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering 1989-1996, 2016:1085-1100. DOI: 10.1177/0954408916653149. (WOS：000414525000001影响因子：1.448)
  32. 姜洪权，王岗，高建民，高智勇，高瑞琪，郭旗. 一种适用于高维非线性特征数据的聚类算法及应用[J],西安交通大学学报. 2017, 2017,51(12):49-55+90. (EI:20181505001141)
  33. Hongquan Jiang, Yalin Zhao*, Jianmin Gao, Zhao Wang.Weld extraction of radiographic images based on Bezier curve fitting and medial axis transformation[J]. INSIGHT,2016,58(10) : 531-535. (WOS:000386592300002, 影响因子: 1.1)
  34. Jiang Hongquan, Zhao Yalin*, Gao Jianmin, Wang Zhao.Weld defect classification based on texture features and principal component analysis[J]. INSIGHT, 2016,58(4),194-200. (WOS:000373565000005, 影响因子: 1.1)
  35. Jiang, Hongquan，Liang Zeming*, Gao Jianmin, Dang Changying. Classification of weld defect based on information fusion technology for radiographic testing system[J]. REVIEW OF SCIENTIFIC INSTRUMENTS. 2016, 87(3):035110. (WOS:000373713300072, 影响因子:1.480).
  36. RX Wang，JM Gao，ZY Gao，G Xu，HQ Jiang. Complex network theory-based condition recognition of electromechanical system in process industry[J]. 中国科学：技术科学英文版, 2016(4):14. DOI: 10.1007/s11431-016-6025-2. (WOS：000373853400011影响因子:1.719)
  37. 高智勇, 霍伟汉, 高建民, 姜洪权. 化工系统海量数据的扩散映射和异常辨识[J]. 计算机集成制造系统, 2014, 020(012):3091-3096. DOI: 10.13196/j.cims.2014.12.020. (EI: 20150700509817)
  38. 姜洪权, 王金宇, 高建民,等. 面向复杂系统故障溯源的SDG-FG模型建模方法[J]. 计算机集成制造系统, 2015, 21(3):9. DOI: 10.13196/j.cims.2015.03.020. (EI: 20151800797440)
  39. 高智勇, 梁银林, 高建民, 姜洪权. 基于集成熵KPCA的复杂机电系统状态监测方法[J]. 计算机集成制造系统, 2015, 21(5):7. DOI: 10.13196/j.cims.2015.05.021. (EI: 20152701006175)
  40. 姜洪权, 王金宇, 高智勇,等. 基于多色集合理论的大型装备IETM数据模块创作技术[J]. 计算机集成制造系统, 2015, 21(6):10. DOI: 10.13196/j.cims.2015.06.016. (EI: 20153901318482)
  41. 姜洪权, 高建民, 梁泽明,等. 基于D-S证据理论的压射工艺模式辨识技术[J]. 计算机集成制造系统, 2015, 21(5):7. DOI: 10.13196/j.cims.2015.05.023. (EI: 20152701006177)
  42. Wang Rongxi, Gao Jianmin, Gao Zhiyong, Gao Xu , Jiang Hongquan, Hilbert-Huang Transform Based Pseudo-Periodic Feature Extraction of Nonlinear Time Series[C]. 2015 Seventh International Conference on Measuring Technology and Mechatronics Automation, 2015: 532-537. DOI: 10.1109/ICMTMA.2015.135.( WOS:000380441700129)
  43. Wang R , Gao J , Gao Z , Jiang H , Cui L . Data fusion based phase space reconstruction from multi-time series[C]. International Journal of Database Theory & Application, 2015. DOI: 10.14257/ijdta.2015.8.6.09.(EI: 20160601905901)
  44. Jiang H , Xu G , Gao Z , et al. A dual-parameter optimization KPCA method for process fault diagnosis[C]. 2015 Annual Reliability and Maintainability Symposium (RAMS), 2015: 1-7, DOI: 10.1109/RAMS.2015.7105134.( WOS:000370748300078)
  45. Mu, Weilei. Gao, Jianmin. Jiang, Hongquan*, et al. A radiographic image quality assessment algorithm based on network topology analysis[J]. Insight, 2014, 56(1):10-14. (WOS:000330315900002, 影响因子:1.1)
  46. Weilei Mu, Jianmin Gao, Hongquan Jiang*, et al. Automatic classification approach to weld defects based on PCA and SVM[J]. Insight, 2013, 55(10):535-539. (WOS:000327050200004, 影响因子:0. 527)
  47. Lv Y , Gao J , Gao Z , Jiang H . Multifractal information fusion based condition diagnosis for process complex systems[J]. Proceedings of Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2013, 227(3):178-184. DOI: 10.1177/0954408912457764. (WOS：000322197800004影响因子:0.305)
  48. Huang X , Gao J , Jiang H , et al. Fault root cause tracing of complicated equipment based on fault graph[J]. Proceedings of Institution of Mechanical Engineers Part E Journal of Process Mechanical Engineering, 2013, 227(1):17-32. DOI: 10.1177/0954408912445957. (WOS：000306959700046影响因子:0.305)
  49. 穆为磊, 高建民, 王昭, 姜洪权，陈富民，党长营. 考虑人眼视觉特性的射线检测数字图像质量评价方法[J]. 西安交通大学学报, 2013, 47(7):5. DOI: 10.7652/xjtuxb201307017. (EI: 20133416650659)
  50. Weilei Mu，Jianmin Gao，Hongquan Jiang，Zhao Wang，Fumin Chen. Automatic classification approach to weld defects based on PCA and SVM[J]. Insight Non Destructive Testing & Condition Monitoring, 2013. DOI: 10.1784/insi.2012.55.10.535. (WOS：000327050200004影响因子:0.545)
  51. Wei-Lei Mu*, Jian-Min Gao, Hong-Quan Jiang, Fu-Min Chen, Zhi-Yong Gao, Kun Chen, Chang-Ying Dang. A Method of Radiographic Image Quality Enhancement[C]. international conference on measuring technology and mechatronics automation, 2013: 29-32. ( EI: 20131816286115)
  52. 穆为磊, 高建民, 王昭, 姜洪权*, 陈富民, 党长营. 考虑人眼视觉特性的射线检测数字图像质量评价方法研究[J]. 西安交通大学学报, 2013, 47(07):91-95. (EI:20133416650659)
  53. 穆为磊,高建民,陈富民*,姜洪权.符合人眼视觉特性的焊缝射线数字图像增强方法[J].西安交通大学学报,2012,46(03):90-93+99.(EI:20121514937930)
  54. Jiang H , Gao J , Mu W , et al. Research on diagnosis of abnormal causes in quality of weld based on the Bayesian net model. IEEE. DOI: 10.1109/ICQR2MSE.2011.5976663. (EI: 20113614308965)
  55. J Pan, H Jiang, J Gao, P Yang, Condition diagnosis with complex network-time series analysis, 2011 Proceedings-Annual Reliability and Maintainability Symposium, 2011: 1-6. DOI: 10.1109/RAMS.2011.5754502.(EI: 20112114009604)
  56. Huang X , Gao J , Jiang H , et al. A systematic fault root causes tracing method for process systems. Proceedings Annual Reliability & Maintainability Symposium, 2011. DOI: 10.1109/RAMS.2011.5754447. (EI: 20112114009550)
  57. Hongquan JIANG*, Jianmin GAO, Weilei MU, Fumin CHEN. Research on diagnosis of abnormal causes in quality of weld based on the Bayesian net model[C]. International conference on quality, reliability, risk, maintenance, and safety engineering, 2011: 505-508. (EI:20113614308965)
  58. Jiang H , Gao J , Gao Z , et al. Condition diagnosis of process industry system with topology analysis based on complex network. 2010. DOI: 10.1109/RAMS.2011.5754502. (EI: 20112114009604)

专利成果：

  1. 姜洪权; 蒲华祥; 高建民; 王荣喜; 胡启航; 刘东程. 一种热量表性能退化评估方法、存储介质及设备. ZL202010937193.9. 已授权.
  2. 姜洪权;高建民;夏锋社;高智勇;王荣喜;周涛;刘东程. 一种基于监测数据多属性特征融合的管道状态识别方法: ZL201910198288.0 已授权.
  3. 高建民;谢军太;高智勇;姜洪权;吕晓喆;王荣喜. 一种基于自组织特征映射的机电系统服役动态标记方法: ZL201810602701.0 已授权.
  4. 姜洪权;高建民;高智勇;王荣喜;周涛;刘东程. 一种慢压射过程稳定性定量分析方法: ZL201810596104.1 已授权.
  5. 姜洪权;高建民;高智勇;王荣喜;李华;程雷;贺帅. 基于灰度变化特征分析的无损射线底片灵敏度判别方法: ZL201810940669.7 已授权.
  6. 姜洪权; 高建民; 高智勇; 王荣喜; 刘东程; 周涛; 梁泽明. 一种基于多属性的设备性能退化评估方法: ZL201810785788.X 已授权.
  7. 高智勇;李鼎;梁艳杰;亢嘉妮;王荣喜;高建民;姜洪权. 一种基于耦合关系的流程工业生产系统数据监测方法: ZL201810442946.1. 已授权.
  8. 姜洪权,王鹏星,高建民,王荣喜,王瑜,支泽林,武小赛,胡启航. 一种射线图像焊缝区域缺陷探测方法、存储介质及设备：中国，ZL202010809005.4.已授权.
  9. 姜洪权,高建民,高智勇,王昭,王荣喜,程雷,昌亚胜,贺帅. 基于天牛须算法和数学形态学的射线图像焊缝提取方法：中国,ZL201810367303.5.已授权.
  10. 姜洪权,高建民,高智勇,王昭,王荣喜,贺帅,昌亚胜,程雷. 一种基于改进LeNet-5模型的焊缝缺陷识别方法：中国, ZL201810368229.9.已授权.
  11. 姜洪权,高建民,王晓桥,王泉生,夏锋社,贺帅,程雷,李华,昌亚胜. 一种基于改进卷积神经网络模型的焊缝缺陷识别方法：中国, CN201810709776.9.已授权.
  12. 姜洪权,高建民,王晓桥,王泉生,夏锋社,程雷,贺帅,李华. 一种基于边缘检测的射线图像标记信息字符分割方法：中国，CN201811573372.8.已授权.
  13. 高建民;李云龙;姚卫杰;姜洪权;王昭;高智勇. 一种自动遮光调节装置及其控制方法:中国,ZL2018103210457. 已授权
  14. 王荣喜;高建民;高智勇;姜洪权;彭财元. 一种复杂机电系统服役模式自动识别方法:中国, ZL201811223045.X.已授权.
  15. 高建民;谢军太;高智勇;姜洪权;陈琨;冯龙飞. 一种基于自适应符号传递熵的机电系统交互网络建模方法:中国, ZL201810954284.6.已授权.
  16. 姜洪权;高建民;高智勇;王荣喜;周涛;刘东程;梁泽明. 一种基于多退化样本数据融合的热量表剩余寿命预测方法:中国, ZL201810863794.2.已授权.
  17. 高智勇;高建民;曹杰;姜洪权;谢军太. 一种基于形位公差与尺寸公差的机械零件选配方法:中国, ZL201810771854.8.已授权.
  18. 高智勇;高建民;姜洪权;陈富民;江遥;梁泽明;马冬媛;高瑞琪. 一种基于PCA-Cpk的冷连轧生产线服役质量状态评估方法: ZL201710035596.2 已授权.
  19. 高智勇;谢军太;高建民;姜洪权;王荣喜;冯龙飞. 基于网络结构熵的流程工业机电系统耦合状态评估方法:中国,ZL201810508428.5.已授权.
  20. 姜洪权;高建民;高智勇;张雪微;梁泽明;姜朋;高瑞琪. 一种综合质量目标与设备性能的挤出吹塑工艺优化方法:中国, ZL2017100360142.已授权.
  21. 王荣喜;高建民;姜洪权;高智勇;陈琨. 一种可靠性敏感度驱动的高端复杂装备可靠性分析方法:中国, ZL201711332578.7.已授权.
  22. 高智勇;高瑞琪;梁泽明;亢嘉妮;姜朋. 一种基于改进PCA的带钢冷轧质量问题溯源及控制方法:中国, ZL201710035587.3.已授权.
  23. 王荣喜;高建民;高智勇;姜洪权. 一种数据驱动的复杂机电系统服役质量状态评估方法:中国, ZL201611249707.1.已授权.
  24. 姜洪权;高建民;王宏叶;梁泽明;张雪微;刘文强;张凡勇;高瑞琪. 一种适用于多品种计量仪表通讯的参数动态解析方法: 中国, ZL2016103785347.已授权.
  25. 姜洪权，高建民，梁泽明，麻兴斌，王俊等.基于机器视觉的工业产品外形直径在线检测的方法:中国，CN201410454075.7.已授权.
  26. 高建民，王昭，陈富民，苏赵等．工业射线检测底片数字化装置：中国，201310117881.0[P]．已授权.
  27. 高建民,王昭,陈富民,党长营等.工业射线检测底片数字化装置:：中国, ZL201210012776.6 [P].已授权.
  28. 高建民，孙锴，高智勇，陈富民，姜洪权等.基于二维彩色数字图谱的复杂机电系统状态评估方法：中国，ZL201110146488.5.已授权
  29. 陈富民;高建民;高智勇;姜洪权. 一种流程工业系统的过程故障分析装置及方法:中国，ZL200810232132.1.已授权.
  30. 高建民，陈富民，申清明等．西安交通大学．基于射线衰减能量场的无损检测缺陷提取、识别方法：中国，200710018884.3[P]．已授权．
  31. 李成，高建民，陈富民，陈琨，申清明等．西安交通大学．工业射线检测底片数字化装置：中国，200710018919.3[P]．已授权．

软件著作权成果：

  1. 基于射线检测数字图像的复杂金属构件缺陷类型识别系统V1.0:中国, 2023SR0236946.已授权.
  2. 压力球罐TOFD图谱预处理系统V1.0:中国, 2022SR1462666.已授权.
  3. 基于TOFD图谱的压力球罐缺陷目标检测系统V1.0:中国, 2022SR1462315.已授权.
  4. 复杂金属构件射线检测数字图像预处理系统V1.0:中国, 2022SR0169648.已授权.
  5. 基于射线检测图像的复杂金属构件缺陷类型识别系统. 中国,2021SR1616025.已授权.
  6. 流程工业压力容器服役质量评估与管理系统 V1.0:中国, 2020SR0641628.已授权.
  7. 多源信息融合的焊接质量综合评价系统V1.0:中国,2020SR0884541[P].已授权.
  8. 射线底片字符信息自动识别软件V1.0: 中国,2020SR0727684[P]. 已授权.
  9. 射线底片数字化图像综合预处理系统V1.0:中国,2020SR1211282[P].已授权.
  10. 无损检测资源与质量管理系统:中国, 2018SR562907.已授权.
  11. 数据驱动的复杂机电系统服役质量状态评估系统软件:中国,2017SR575000.已授权.
  12. 冷轧带钢板厚在线质量判定系统:中国,2017SR234475.已授权.
  13. 冷轧带钢板面在线质量判定系统:中国,2017SR234567.已授权.
  14. 冷轧带钢板形在线质量判定系统:中国,2017SR234485.已授权.
  15. 冷轧带钢生产线运行状态监测与分析智能化系统V1.0:中国,2017SR037557.已授权.
  16. 冷轧带钢在线质量综合评价系统:中国,2017SR289847.已授权.
  17. 冷轧带钢质量工艺保证系统:中国,2017SR022863.已授权.
  18. 冷轧带钢质量问题原因追溯系统:中国,2017SR022864.已授权.
  19. 挤吹塑产品生产过程壁厚控制系统V1.0:中国,2016SR141123.已授权.
  20. 挤出吹塑产品关键工艺参数监测系统:中国,2016SR145548.已授权.
  21. 数字化车间认知管理系统软件V1.0:中国,2016SR194724.已授权.
  22. 挤压机闭环实时控制系统软件V1.0:中国, 2016SR195354.已授权.
  23. 热量表耐久性实验装置运行状态监测系统V1.0:中国,2016SR148661.已授权.
  24. 热量表耐久性实验仪表状态信息监测系统V1.0:中国,2016SR148633.已授权.
  25. 热量表可靠性实验变频控制系统V1.0:中国,2016SR148634.已授权.
  26. 在线SPC及数字化车间教学系统V1.0:中国, 2016SR144708.已授权.
  27. 企业多态多工位质量数据分析平台软件:中国,2015SR038821.已授权.
  28. 压铸机监测系统客户端软件V2.0:中国,2015SR193638.已授权.
  29. 压铸机闭环监控系统软件V2.0:中国,2015SR073632.已授权.
  30. 压铸机专家知识库客户端软件V1.0:中国,2015SR073686.已授权.
  31. 图像处理圆弧测量系统客户端软件V1.0:中国,2014SR103383.已授权
  32. 吹塑瓶外观质量监测系统客户端软件V1.0:中国,2014SR103915.已授权.
  33. 能源化工企业生产系统状态分析与监控软件:中国,2014SR167656.已授权.
  34. 数据驱动的复杂机电系统生产监控分析软件:中国,2013SR061757.已授权.
  35. 射线底片数字化软件V2.0:中国, 2012SR062667[P].已授权.
  36. 计算机辅助评片专家系统软件V2.0:中国, 2012SR109756[P].已授权.