Laurence Tianruo Yang - Selected Publications#

Cyber-Physical-Social systems (CPSS), as a novel emerging paradigm, have gained popularity within the research community and industry since it enables deep fusion among humans, computers, and things.

CPSS Modelling and Design:

He has made pioneering contributions to study effective and efficient approaches for CPSS modeling and general system design automation methods, as well as methods analyzing and/or improving their power and energy, security, trust and reliability features. Some main representative works are listed as follows:

[1] Jing Zeng, Laurence T. Yang, Jianhua Ma, and Minyi Guo, "HyperspaceFlow: A System-level Design Methodology for Smart Space," IEEE Transactions on Emerging Topics in Computing, 2016, 4(4): 568-583. (IEEE TCSC Best Journal Paper Award 2016).

[2] Liwei Kuang, Fei Hao, Laurence T. Yang, Man Lin, Changqing Luo, and Geyong Min, "A Tensor-based Approach for Big Data Representation and Dimensionality Reduction," IEEE Transactions on Emerging Topics in Computing, 2014, 2(3): 280-291. (IEEE TCSC Most Influential Paper Award 2017).

[3] Qingchen Zhang, Laurence T. Yang, and Zhikui Chen, "Privacy Preserving Deep Computation Model on Cloud for Big Data Feature Learning," IEEE Transactions on Computers, 2016, 65(5):1351-1362. (ESI Highly Cited Paper, top 1% citation).

CPSS Data Analytics:

The ultimate objective of CPSS is to provide proactive, smart, and pervasive services to users. He developed a systematic tensor-based Big Data-as-a-Service framework for representing, processing and analyzing big data generated from CPSS, which offers feedbacks to cyber, physical, social and control flows and therefore to refine the previously designed CPSS. Some main representative works are listed as follows:

[4] Qingchen Zhang, Laurence T. Yang, Zhikui Chen, and Peng Li, "An Improved Deep Computation Model Based on Canonical Polyadic Decomposition," IEEE Transactions on Systems, Man, and Cybernetics: Systems, 2018, 48(10):1657-1666. (ESI Highly Cited Paper, top 1% citation).

[5] Peng Li, Zhikui Chen, Laurence T. Yang, Qingchen Zhang, and M. Jamal Deen, "Deep Convolutional Computation Model for Feature Learning on Big Data in Internet of Things," IEEE Transactions on Industrial Informatics, 2017, 13(3):1193-1201. (ESI Highly Cited Paper, top 1% citation).

[6] Qingchen Zhang, Chunsheng Zhu, Laurence T. Yang, Zhikui Chen, Liang Zhao, and Peng Li, “An Incremental CFS Algorithm for Clustering Large Data in Industrial Internet of Things,” IEEE Transactions on Industrial Informatics, 2017, 13(3): 1193-1201. (IEEE TCCPS Most Influential Paper Award 2017, ESI Highly Cited Paper, top 0.1% citation).

[7] Qingchen Zhang, Laurence T. Yang, Zhikui Chen, Peng Li, and Fanyu Bu, “An Adaptive Dropout Deep Computation Model for Industrial IoT Big Data Learning with Crowdsourcing to Cloud Computing,” IEEE Transactions on Industrial Informatics, 2019, 15(4): 2330-2337. (ESI Highly Cited Paper, top 1% citation).

[8]] Qingchen Zhang, Laurence T. Yang, Zhikui Chen, Peng Li, and M. Jamal Deen, “Privacy-Preserving Double-Projection Deep Computation Model with Crowdsourcing on Cloud for Big Data Feature Learning,” IEEE Internet of Things Journal, 2018, 5(4): 2896-2903. (ESI Highly Cited Paper, top 1% citation).

[9] Qingchen Zhang, Laurence T. Yang, and Zhikui Chen, “Deep Computation Model for Unsupervised Feature Learning on Big Data,” IEEE Transactions on Services Computing, 2016, 9(1): 161-171. (IEEE TCSTC Most Influential Paper Award).

[10] Huazhong Liu, Baoshun Liu, Laurence T. Yang, Man Lin, Yuhui Deng, Kashif Bilal, and Samee U. Khan, “Thermal-Aware and DVFS-Enabled Big Data Task Scheduling for Data Centers,” IEEE Transactions on Big Data, 2018, 4(2):177-190. (The 2019 IEEE ComSoc TCBD Best Journal Paper).