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姓 名:康兵义

职 称:副教授

办公室:信息工程学院104办公室

电 话:

邮 箱:bingyi.kang@nwsuaf.edu.cn

 

基本信息

    康兵义,男,河南信阳人,博士,副教授,硕士研究生导师,IEEE会员。2016 年赴英属哥伦比亚大学公派联合培养,2018 年毕业于西南大学数学(智能计算与复杂系统)专业,获理学博士学位,2018 年入职西北农林科技大学信息工程学院,讲师。2020年破格晋升副教授。作为项目负责人主持国家自然科学基金青年项目1项,中国博士后基金面上项目1项,西北农林科技大学博士科研启动基金1项。获陕西省科学技术二等奖、领跑者F5000等省部级奖励和荣誉。目前以第一作者或通讯作者在IEEE Transactions on Fuzzy Systems,Information Sciences,Knowledge-Based Systems,Applied Mathematics and Computation,电子学报等期刊公开发表30余篇学术论文,GOOGLE 学术引用1400余次。受邀担任IEEE Transactions on Fuzzy Systems,Information Sciences,International Journal of Approximate Reasoning, Robotics and Autonomous Systems,Computers & Industrial Engineering 等多个期刊审稿人。


研究方向

研究兴趣:多源信息融合;多属性智能决策;不确定信息处理;智能信息处理;目标识别与风险评估;模糊概率近似推理;机器学习算法与应用


招生信息

1)拟招收学术型硕士研究生1-2名,专业硕士研究生2-3名,实行师生互选。

2)实验室全年招收本科科研助理,要求学习成绩排名靠前(年级成绩专业前10),外语水平较好。

3)欢迎立志从事科研,将来希望以高等院校教师和科研院所科技人员为职业方向,打算硕士/本科毕业后攻读博士学位或是海外深造的学生。


开设课程

操作系统(本科生)

最优化技术与数学建模(研究生)

 

学术成果

一、部分发表科研论文

[1] Ruolan Cheng, Jianfeng Zhang, Bingyi Kang*, Ranking of Z-numbers Based on the Developed Golden Rule Representative Value[J], IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2022.3170208, Accept, 2022.

[2] Yanan Li, Ruonan Zhu, Xiangjun Mi, Bingyi Kang*, An intelligent quality-based fusion method for complex-valued distributions using POWA operator[J], Engineering Applications of Artificial Intelligence, vol. 109, pp. 104618, 2022.

[3] Huizi Cui, Lingge Zhou, Yan Li, Bingyi Kang*, Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis[J], Chaos, Solitons & Fractals, vol. 155, pp. 111736, 2022.

[4] Ruolan Cheng, Bingyi Kang*, Jianfeng Zhang*, A novel method to rank fuzzy numbers using the developed golden rule representative value[J], Applied Intelligence, Online, DOI: doi.org/10.1007/s10489-021-02965-4, 2022.

[5] Ruonan Zhu, Yanan Li, Ruolan Cheng, Bingyi Kang*, An improved model in fusing multi-source information based on Z-numbers and POWA operator[J], Computational and Applied Mathematics, vol. 41, no. 1, pp. 1-28, 2022.

[6] Ye Tian, Xiangjun Mi, Huizi Cui, Pengdan Zhang, Bingyi Kang*, Using Z-number to measure the reliability of new information fusion method and its application in pattern recognition[J], Applied Soft Computing, vol.111, pp.107658, 2021.

[7] Lingge Zhou, Huizi Cui, Chongru Huang, Bingyi Kang*, Jianfeng Zhang*, Counter Deception in Belief Functions Using Shapley Value Methodology[J], International Journal of Fuzzy Systems, vol.24, pp. 340–354, 2022.

[8] Chongru Hang, Xiangjun Mi, Bingyi Kang*, Basic probability assignment to Probability distribution function based on the Shapley value approach [J],International Journal of Intelligent Systems, vol. 36, no. 8, pp. 4210-4236, 2021.

[9] Yingfu Wu, Bingyi Kang*, Hao Wu*, Strategies of Attack-Defense Game for Wireless Sensor Networks Considering the Effect of Confidence Level in Fuzzy Environment[J], Engineering Applications of Artificial Intelligence, vol.102, pp. 104238, 2021.

[10] Ye Tian, Xiangjun Mi, Yunpeng Ji, Bingyi Kang*, Z^E-number: A new extended Z-number and its application on multiple attribute group decision making[J], Engineering Applications of Artificial Intelligence, vol.101, pp.104225, 2021.

[11] Xiangjun Mi, Ye Tian and Bingyi Kang*, MADA problem: A new scheme based on D numbers and aggregation functions[J], Journal of Intelligent &Fuzzy Systems,  vol. 40, no. 6, pp. 11231-11255, 2021.

[12] Pengdan Zhang, Qing Liu and Bingyi Kang*, An improved OWA-Fuzzy AHP decision model for multi-attribute decision making problem[J], Journal of Intelligent & Fuzzy Systems,  vol. 40, no. 5, pp. 9655-9668, 2021.

[13] Xiangjun Mi, Ye Tian and Bingyi Kang*, A hybrid multi-criteria decision making approach for assessing health-care waste management technologies based on soft likelihood function and D-numbers[J], Applied Intelligence, vol. 51, pp. 6708–6727, 2021.

[14] Xiangjun Mi, Tongxuan Lv, Ye Tian and Bingyi Kang*, Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system [J], ISA Transactions, vol.112, pp. 137-149, 2021.

[15] Xiangjun Mi, and Bingyi Kang*, On the belief universal gravitation (BUG) [J], Computers & Industrial Engineering, vol. 148, pp. 106685, 2020.

[16] Ye Tian, Xiangjun Mi, Lili Liu and Bingyi Kang*, A new soft likelihood function based on D numbers in handling uncertain information [J], International Journal of Fuzzy Systems, vol. 22, no. 7, pp. 2333–2349, 2020.

[17] Qing Liu, Huizi Cui, Ye Tian and Bingyi Kang*, On the Negation of discrete Z-numbers [J], Information Sciences, vol. 537, pp. 18-29, 2020.

[18] Ye Tian, Lili Liu, Xiangjun Mi and Bingyi Kang*, ZSLF : A new soft likelihood function based on Z-numbers and its application in expert decision system [J], IEEE Transactions on Fuzzy Systems, vol. 29, no. 8, pp. 2283-2295, 2020.

[19] Ye Tian and Bingyi Kang*, A modified method of generating Z-number based on OWA weights and maximum entropy [J], Soft Computing, vol. 24, pp. 15841–15852, 2020.

[20] Xiangjun Mi, and Bingyi Kang*, A modified approach to conflict management from the perspective of non-conflicting element set[J], IEEE ACCESS, vol. 8, pp.  73111-73126, 2020.

[21] Ruonan Zhu, Jiaqi Chen and Bingyi Kang*, Power Law and Dimension of the Maximum Value for Belief Distribution With the Maximum Deng Entropy[J], IEEE ACCESS, vol. 8, pp. 47713 – 47719, 2020.

[22] Pengdan Zhang, Ye Tian and Bingyi Kang*, A New Synthesis Combination Rule Based on Evidential Correlation Coefficient [J], IEEE ACCESS, vol. 8, pp. 39898 – 39906, 2020.

[23] Xiangjun Mi, Ye Tian and Bingyi Kang*, A modified soft‐likelihood function based on POWA operator [J], International Journal of Intelligent Systems, vol. 35, no. 5, pp. 869-890, 2020.

[24] Jing Zhang, Ruqin Liu, Jianfeng Zhang and Bingyi Kang*, Extension of Yager's Negation of a Probability Distribution Based on Tsallis Entropy[J], International Journal of Intelligent Systems, vol. 35, no. 1, pp. 72-84, 2020.

[25] Bingyi Kang*, Pengdan Zhang, Zhenyu Gao, Gyan  Chhipi-Shrestha, Kasun Hewage and Rehan Sadiq, Environmental assessment under uncertainty using Dempster–Shafer theory and Z-numbers[J], Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 5, pp. 2041-2060, 2020.

[26] Bingyi Kang, Yong Deng, The maximum Deng entropy[J], IEEE ACCESS, vol. 7, no. 1, pp. 120758-120765, 2019.

[27] Qing Liu, Ye Tian and Bingyi Kang*, Derive knowledge of Z-number from the perspective of Dempster-Shafer evidence theory [J], Engineering Applications of Artificial Intelligence, vol. 85, pp. 754-764, 2019.

[28] Huizi Cui, Qing Liu, Jianfeng Zhang and Bingyi Kang*, An improved Deng entropy and its application in pattern recognition[J], IEEE Access, 2019, 7: 18284-18292.

[29] Bingyi Kang, Yong Deng, Kasun Hewage, and Rehan Sadiq. A method of measuring uncertainty for Z-number[J]. IEEE Transactions on Fuzzy Systems. vol. 27, no. 4, pp. 731-738, 2019.

[30] Bingyi Kang, Gyan Chhipi-Shrestha, Yong Deng, Julie Mori, Kasun Hewage, and Rehan Sadiq. Development of a predictive model for Clostridium difficile infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory[J]. Stochastic Environmental Research and Risk Assessment. vol. 32, no. 6, pp. 1743-1758, 2018.

[31] Bingyi Kang, Gyan Chhipi-Shrestha, Yong Deng, Kasun Hewage, and Rehan Sadiq. Stable strategies analysis based on the utility of Z-number in the evolutionary games[J]. Applied Mathematics and Computation. vol. 324, pp. 202-217, 2018.

[32] Bingyi Kang, Yong Deng, Kasun Hewage, and Rehan Sadiq. Generating Z‐number based on OWA weights using maximum entropy[J]. International Journal of Intelligent Systems. vol. 33, no. 8, pp. 1745-1755, 2018.

[33] Bingyi Kang, Yong Deng, and Rehan Sadiq. Total utility of Z-number[J]. Applied Intelligence. vol. 48, no. 3, pp. 703-729, 2017.

[34] Bingyi Kang, Yong Deng, Rehan Sadiq, and Sankaran Mahadevan. Evidential cognitive maps[J]. Knowledge-Based Systems. vol. 35, pp. 77-86, 2012.

[35] 康兵义, 李娅, 邓勇, 张雅娟, 邓鑫洋. 基于区间数的基本概率指派生成方法及应用[J]. 电子学报. vol. 400 no. 6, pp. 1092-1096,2012.


二、已主持科研项目

[1] 国家自然科学基金青年项目(No. 61903307),《基于信度函数理论的Z-数不确定信息融合研究》,2020年1月至2022年12月,主持。

[2] 中国博士后科学基金资助项目(No. 2020M683575),《基于信度函数理论的混合不确定信息融合研究》,2020年11月至2022年6月,主持。

[3] 陕西省专项项目(No.F2020221005)《面向信度函数理论的信息融合反欺骗机制研究》,2021年1月至2023年12月,主持。

[4] 西北农林科技大学科研启动基金(No. Z109021812),《基于模糊认知图的关联证据融合研究》,2018 年 7 月至 2020 年6月,主持。



相关链接

Google学术

https://scholar.google.ca/citations?user=VIEDOvoAAAAJ&hl=en

ResearchGate社区

https://www.researchgate.net/profile/Bingyi-Kang



在读学生

2021级研究生:崔惠子,肖子龙,赵萱



康兵义电子证件照 - 副本.JPG

Name: Bingyi Kang

Professional Title: Associate Professor

Office: COIE-104

Tel:

Email:bingyi.kang@nwsuaf.edu.cn

 

Personal Information

  Dr. Bingyi Kang is now an Associate Professor of the College of Information Engineering at Northwest A&F University, Yangling, Shaanxi, China. He obtained his Ph.D. at Southwest University, Chongqing, China, in 2018. He was also a Visiting International Research Student (Joint Ph.D. student sponsored by CSC) at the University of British Columbia (Okanagan Campus) in 2016. Dr. Kang's research interest includes multi-source information fusion and intelligence information processing. He has published several papers in the journals such as IEEE Transactions on Fuzzy Systems, Knowledge-Based Systems,Applied Mathematics and Computation,Applied Intelligence,Stochastic Environmental Research and Risk Assessment. He has been invited as a reviewer for the journals, e.g. IEEE Transactions on Fuzzy Systems, Information Sciences, International Journal of Approximate Reasoning, Robotics and Autonomous Systems, Computers & Industrial Engineering.

 

Research Directions

Dr. Kang's research interest includes Multi-Source Information Fusion, Multi-Criteria Decision Making, Uncertain Information Processing, Intelligence Information Processing, Intelligent Computing.

 

Curriculum

Operating System (Undergraduate)

Optimization Techniques and Mathematical Modeling (Postgraduate)

 

Academic Achievement

Selected Publications:

[1] Ruolan Cheng, Jianfeng Zhang, Bingyi Kang*, Ranking of Z-numbers Based on the Developed Golden Rule Representative Value[J], IEEE Transactions on Fuzzy Systems, DOI: 10.1109/TFUZZ.2022.3170208, Accept, 2022.

[2] Yanan Li, Ruonan Zhu, Xiangjun Mi, Bingyi Kang*, An intelligent quality-based fusion method for complex-valued distributions using POWA operator[J], Engineering Applications of Artificial Intelligence, vol. 109, pp. 104618, 2022.

[3] Huizi Cui, Lingge Zhou, Yan Li, Bingyi Kang*, Belief entropy-of-entropy and its application in the cardiac interbeat interval time series analysis[J], Chaos, Solitons & Fractals, vol. 155, pp. 111736, 2022.

[4] Ruolan Cheng, Bingyi Kang*, Jianfeng Zhang*, A novel method to rank fuzzy numbers using the developed golden rule representative value[J], Applied Intelligence, Online, DOI: doi.org/10.1007/s10489-021-02965-4, 2022.

[5] Ruonan Zhu, Yanan Li, Ruolan Cheng, Bingyi Kang*, An improved model in fusing multi-source information based on Z-numbers and POWA operator[J], Computational and Applied Mathematics, vol. 41, no. 1, pp. 1-28, 2022.

[6] Ye Tian, Xiangjun Mi, Huizi Cui, Pengdan Zhang, Bingyi Kang*, Using Z-number to measure the reliability of new information fusion method and its application in pattern recognition[J], Applied Soft Computing, vol.111, pp.107658, 2021.

[7] Lingge Zhou, Huizi Cui, Chongru Huang, Bingyi Kang*, Jianfeng Zhang*, Counter Deception in Belief Functions Using Shapley Value Methodology[J], International Journal of Fuzzy Systems, vol.24, pp. 340–354, 2022.

[8] Chongru Hang, Xiangjun Mi, Bingyi Kang*, Basic probability assignment to Probability distribution function based on the Shapley value approach [J],International Journal of Intelligent Systems, vol. 36, no. 8, pp. 4210-4236, 2021.

[9] Yingfu Wu, Bingyi Kang*, Hao Wu*, Strategies of Attack-Defense Game for Wireless Sensor Networks Considering the Effect of Confidence Level in Fuzzy Environment[J], Engineering Applications of Artificial Intelligence, vol.102, pp. 104238, 2021.

[10] Ye Tian, Xiangjun Mi, Yunpeng Ji, Bingyi Kang*, Z^E-number: A new extended Z-number and its application on multiple attribute group decision making[J], Engineering Applications of Artificial Intelligence, vol.101, pp.104225, 2021.

[11] Xiangjun Mi, Ye Tian and Bingyi Kang*, MADA problem: A new scheme based on D numbers and aggregation functions[J], Journal of Intelligent &Fuzzy Systems,  vol. 40, no. 6, pp. 11231-11255, 2021.

[12] Pengdan Zhang, Qing Liu and Bingyi Kang*, An improved OWA-Fuzzy AHP decision model for multi-attribute decision making problem[J], Journal of Intelligent & Fuzzy Systems,  vol. 40, no. 5, pp. 9655-9668, 2021.

[13] Xiangjun Mi, Ye Tian and Bingyi Kang*, A hybrid multi-criteria decision making approach for assessing health-care waste management technologies based on soft likelihood function and D-numbers[J], Applied Intelligence, vol. 51, pp. 6708–6727, 2021.

[14] Xiangjun Mi, Tongxuan Lv, Ye Tian and Bingyi Kang*, Multi-sensor data fusion based on soft likelihood functions and OWA aggregation and its application in target recognition system [J], ISA Transactions, vol.112, pp. 137-149, 2021.

[15] Xiangjun Mi, and Bingyi Kang*, On the belief universal gravitation (BUG) [J], Computers & Industrial Engineering, vol. 148, pp. 106685, 2020.

[16] Ye Tian, Xiangjun Mi, Lili Liu and Bingyi Kang*, A new soft likelihood function based on D numbers in handling uncertain information [J], International Journal of Fuzzy Systems, vol. 22, no. 7, pp. 2333–2349, 2020.

[17] Qing Liu, Huizi Cui, Ye Tian and Bingyi Kang*, On the Negation of discrete Z-numbers [J], Information Sciences, vol. 537, pp. 18-29, 2020.

[18] Ye Tian, Lili Liu, Xiangjun Mi and Bingyi Kang*, ZSLF : A new soft likelihood function based on Z-numbers and its application in expert decision system [J], IEEE Transactions on Fuzzy Systems, vol. 29, no. 8, pp. 2283-2295, 2020.

[19] Ye Tian and Bingyi Kang*, A modified method of generating Z-number based on OWA weights and maximum entropy [J], Soft Computing, vol. 24, pp. 15841–15852, 2020.

[20] Xiangjun Mi, and Bingyi Kang*, A modified approach to conflict management from the perspective of non-conflicting element set[J], IEEE ACCESS, vol. 8, pp.  73111-73126, 2020.

[21] Ruonan Zhu, Jiaqi Chen and Bingyi Kang*, Power Law and Dimension of the Maximum Value for Belief Distribution With the Maximum Deng Entropy[J], IEEE ACCESS, vol. 8, pp. 47713 – 47719, 2020.

[22] Pengdan Zhang, Ye Tian and Bingyi Kang*, A New Synthesis Combination Rule Based on Evidential Correlation Coefficient [J], IEEE ACCESS, vol. 8, pp. 39898 – 39906, 2020.

[23] Xiangjun Mi, Ye Tian and Bingyi Kang*, A modified soft‐likelihood function based on POWA operator [J], International Journal of Intelligent Systems, vol. 35, no. 5, pp. 869-890, 2020.

[24] Jing Zhang, Ruqin Liu, Jianfeng Zhang and Bingyi Kang*, Extension of Yager's Negation of a Probability Distribution Based on Tsallis Entropy[J], International Journal of Intelligent Systems, vol. 35, no. 1, pp. 72-84, 2020.

[25] Bingyi Kang*, Pengdan Zhang, Zhenyu Gao, Gyan  Chhipi-Shrestha, Kasun Hewage and Rehan Sadiq, Environmental assessment under uncertainty using Dempster–Shafer theory and Z-numbers[J], Journal of Ambient Intelligence and Humanized Computing, vol. 11, no. 5, pp. 2041-2060, 2020.

[26] Bingyi Kang, Yong Deng, The maximum Deng entropy[J], IEEE ACCESS, vol. 7, no. 1, pp. 120758-120765, 2019.

[27] Qing Liu, Ye Tian and Bingyi Kang*, Derive knowledge of Z-number from the perspective of Dempster-Shafer evidence theory [J], Engineering Applications of Artificial Intelligence, vol. 85, pp. 754-764, 2019.

[28] Huizi Cui, Qing Liu, Jianfeng Zhang and Bingyi Kang*, An improved Deng entropy and its application in pattern recognition[J], IEEE Access, 2019, 7: 18284-18292.

[29] Bingyi Kang, Yong Deng, Kasun Hewage, and Rehan Sadiq. A method of measuring uncertainty for Z-number[J]. IEEE Transactions on Fuzzy Systems. vol. 27, no. 4, pp. 731-738, 2019.

[30] Bingyi Kang, Gyan Chhipi-Shrestha, Yong Deng, Julie Mori, Kasun Hewage, and Rehan Sadiq. Development of a predictive model for Clostridium difficile infection incidence in hospitals using Gaussian mixture model and Dempster–Shafer theory[J]. Stochastic Environmental Research and Risk Assessment. vol. 32, no. 6, pp. 1743-1758, 2018.

[31] Bingyi Kang, Gyan Chhipi-Shrestha, Yong Deng, Kasun Hewage, and Rehan Sadiq. Stable strategies analysis based on the utility of Z-number in the evolutionary games[J]. Applied Mathematics and Computation. vol. 324, pp. 202-217, 2018.

[32] Bingyi Kang, Yong Deng, Kasun Hewage, and Rehan Sadiq. Generating Z‐number based on OWA weights using maximum entropy[J]. International Journal of Intelligent Systems. vol. 33, no. 8, pp. 1745-1755, 2018.

[33] Bingyi Kang, Yong Deng, and Rehan Sadiq. Total utility of Z-number[J]. Applied Intelligence. vol. 48, no. 3, pp. 703-729, 2017.

[34] Bingyi Kang, Yong Deng, Rehan Sadiq, and Sankaran Mahadevan. Evidential cognitive maps[J]. Knowledge-Based Systems. vol. 35, pp. 77-86, 2012.

[35] Bingyi Kang , Ya Li, Yong Deng, et al. Determination of Basic Probability Assignment Based on Interval Numbers and Its Application[J]. Acta Electronica Sinica. vol. 40, no. 6, pp. 1092-1096,2012. (EI index)


 

 

Links

Google Scholar

https://scholar.google.ca/citations?user=VIEDOvoAAAAJ&hl=en

ResearchGate

https://www.researchgate.net/profile/Bingyi-Kang