Ji-yao An
Orcid: 0000-0002-9439-9563
According to our database1,
Ji-yao An
authored at least 31 papers
between 2006 and 2025.
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Bibliography
2025
ExFuzzGCN: An interpretable and expressive fuzzy-based GCN for intelligent traffic cognition.
Inf. Sci., 2025
2023
Reliability Modeling and Assessment for a Cyber-Physical System With a Complex Boundary Behavior.
IEEE Trans. Reliab., March, 2023
2022
An Online and Scalable Model for Generalized Sparse Nonnegative Matrix Factorization in Industrial Applications on Multi-GPU.
IEEE Trans. Ind. Informatics, 2022
DGInet: Dynamic graph and interaction-aware convolutional network for vehicle trajectory prediction.
Neural Networks, 2022
2021
IEEE Trans. Sustain. Comput., 2021
A DVFS-Weakly Dependent Energy-Efficient Scheduling Approach for Deadline-Constrained Parallel Applications on Heterogeneous Systems.
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2021
IGAGCN: Information geometry and attention-based spatiotemporal graph convolutional networks for traffic flow prediction.
Neural Networks, 2021
2020
Quantitative Modeling and Analytical Calculation of Anelasticity for a Cyber-Physical System.
IEEE Trans. Syst. Man Cybern. Syst., 2020
IEEE Trans. Intell. Transp. Syst., 2020
2019
Resource-Cost-Aware Fault-Tolerant Design Methodology for End-to-End Functional Safety Computation on Automotive Cyber-Physical Systems.
ACM Trans. Cyber Phys. Syst., 2019
Optimal power allocation and load balancing for non-dedicated heterogeneous distributed embedded computing systems.
J. Parallel Distributed Comput., 2019
An efficient manifold regularized sparse non-negative matrix factorization model for large-scale recommender systems on GPUs.
Inf. Sci., 2019
Fuzzy-Based Hybrid Location Algorithm for Vehicle Position in VANETs via Fuzzy Kalman Filtering Approach.
Adv. Fuzzy Syst., 2019
IEEE Access, 2019
A Novel Fuzzy Approach for Combining Uncertain Conflict Evidences in the Dempster-Shafer Theory.
IEEE Access, 2019
A Novel Fuzzy-Based Convolutional Neural Network Method to Traffic Flow Prediction With Uncertain Traffic Accident Information.
IEEE Access, 2019
2018
MSGD: A Novel Matrix Factorization Approach for Large-Scale Collaborative Filtering Recommender Systems on GPUs.
IEEE Trans. Parallel Distributed Syst., 2018
Message response time analysis for automotive cyber-physicalsystems with uncertain delay: An <i>M/PH/1</i> queue approach.
Perform. Evaluation, 2018
A novel fuzzy deep-learning approach to traffic flow prediction with uncertain spatial-temporal data features.
Future Gener. Comput. Syst., 2018
A Novel Stability Criterion for a Class of Networked Control Systems Using Some Integral Inequalities.
Proceedings of the 27th IEEE International Symposium on Industrial Electronics, 2018
CUSNTF: A Scalable Sparse Non-negative Tensor Factorization Model for Large-scale Industrial Applications on Multi-GPU.
Proceedings of the 27th ACM International Conference on Information and Knowledge Management, 2018
2017
Scheduling Algorithms of Flat Semi-Dormant Multicontrollers for a Cyber-Physical System.
IEEE Trans. Ind. Informatics, 2017
IEEE Trans. Computers, 2017
2015
Combination of Evidence with Different Weighting Factors: A Novel Probabilistic-Based Dissimilarity Measure Approach.
J. Sensors, 2015
Improved delay-fractional-dependent stability criteria for systems with nonlinear perturbations.
Proceedings of the 2015 IEEE Conference on Control Applications, 2015
2011
New Results on a Delay-Derivative-Dependent Fuzzy H <sub>∞</sub> Filter Design for T-S Fuzzy Systems.
IEEE Trans. Fuzzy Syst., 2011
A delay-derivative-dependent approach to robust <i>H</i>∞ filter design for uncertain systems with time-varying distributed delays.
J. Frankl. Inst., 2011
Improved stability criteria for time-varying delayed T-S fuzzy systems via delay partitioning approach.
Fuzzy Sets Syst., 2011
2007
Proceedings of the Workshop on Intelligent Information Technology Application, 2007
2006
Proceedings of the 2006 International Conference on Semantics, 2006
Proceedings of the Advances in Neural Networks - ISNN 2006, Third International Symposium on Neural Networks, Chengdu, China, May 28, 2006