Yan-Hui Lin

33 papers Journal 31Unranked 2
YearRankTypeTitle / Venue / Authors
2026 J jnl
Decentralized Federated Learning with Non-IID Data: Challenges, Trends, and Future Opportunities.
ACM Comput. Surv.
Wu-Chun Chung, Chao-Ai Lo, Yan-Hui Lin, Zhi-Hao Chen, Che-Lun Hung
2026 J jnl
Gradient Boosting-Based Predictive Uncertainty Estimation for Tabular Data Using Statistical Variance and Proper Scoring Rule.
IEEE Trans Autom. Sci. Eng.
Peng-Cheng Yan, Enrico Zio, Yan-Hui Lin
2026 J jnl
Recent advances in uncertainty analysis for prognostics and remaining useful life prediction: A review.
Reliab. Eng. Syst. Saf.
Yan-Hui Lin, Peng-Cheng Yan, Enrico Zio
2026 J jnl
Uncertainty-Driven Online Deep Ensemble for Imbalanced Drifting Data Stream Regression.
IEEE Trans. Ind. Informatics
Yan-Hui Lin, Lin Qi
2025 J jnl
A novel method of detection of noisy samples in high-dimensional sequential data considering both attribute and label noise.
Comput. Ind. Eng.
Liang Chang, Lu-Xin Guan, Enrico Zio, Yan-Hui Lin
2025 J jnl
Few-shot remaining useful life prediction based on Bayesian meta-learning with predictive uncertainty calibration.
Eng. Appl. Artif. Intell.
Liang Chang, Yan-Hui Lin
2024 J jnl
An Uncertainty Quantification and Calibration Framework for RUL Prediction and Accuracy Improvement.
IEEE Trans. Instrum. Meas.
Ze-Qi Ding, Qiang Qin, Yifan Zhang, Yan-Hui Lin
2024 J jnl
Enhanced stochastic recurrent hybrid model for RUL Predictions via Semi-supervised learning.
Reliab. Eng. Syst. Saf.
Yan-Hui Lin, Liang Chang, Lu-Xin Guan
2024 J jnl
Reliability Modeling and Parameter Estimation for High-Speed Train Wheels Subject to Multi-Dimensional Degradation Processes Considering Mutual Dependency.
IEEE Trans. Reliab.
Tianli Men, Bin Liu, Yan-Fu Li, Yan-Hui Lin, Ying Zhang
2024 — conf
Ring-Based Decentralized Federated Learning with Cosine Similarity Grouping.
ICCE-Taiwan
Wu-Chun Chung, Yan-Hui Lin, Jau-Ai Luo
2024 J jnl
Uncertainty-Aware Fault Diagnosis Under Calibration.
IEEE Trans. Syst. Man Cybern. Syst.
Yan-Hui Lin, Gang-Hui Li
2023 J jnl
A Semisupervised Deep Hybrid Multitask Model for RUL Prediction.
IEEE Trans. Instrum. Meas.
Yan-Hui Lin, Lu-Xin Guan, Liang Chang, Enrico Zio
2023 J jnl
Ensemble Remaining Useful Life Prediction for Lithium-Ion Batteries With the Fusion of Historical and Real-Time Degradation Data.
IEEE Trans. Veh. Technol.
Yan-Hui Lin, Ling-Ling Tian, Ze-Qi Ding
2023 J jnl
Similarity based remaining useful life prediction based on Gaussian Process with active learning.
Reliab. Eng. Syst. Saf.
Yan-Hui Lin, Ze-Qi Ding, Yan-Fu Li
2022 J jnl
A Bayesian Deep Learning Framework for RUL Prediction Incorporating Uncertainty Quantification and Calibration.
IEEE Trans. Ind. Informatics
Yan-Hui Lin, Gang-Hui Li
2022 J jnl
An Online Transfer Learning Framework for Time-Varying Distribution Data Prediction.
IEEE Trans. Ind. Electron.
Yan-Hui Lin, Liang Chang
2022 J jnl
An Unsupervised Noisy Sample Detection Method for Deep Learning-Based Health Status Prediction.
IEEE Trans. Instrum. Meas.
Yan-Hui Lin, Liang Chang
2022 J jnl
Remaining useful life prediction for complex systems considering varying future operational conditions.
Qual. Reliab. Eng. Int.
Liang Chang, Yan-Hui Lin, Enrico Zio
2021 J jnl
A generalized petri net-based modeling framework for service reliability evaluation and management of cloud data centers.
Reliab. Eng. Syst. Saf.
Xiao-Yang Li, Yue Liu, Yan-Hui Lin, Lianghua Xiao, Enrico Zio, Rui Kang
2021 J jnl
Adaptive Kernel Auxiliary Particle Filter Method for Degradation State Estimation.
Reliab. Eng. Syst. Saf.
Yan-Hui Lin, Xin-Lei Jiao
2021 J jnl
Dynamic Mode Transfer Scheduling for Degrading Standby System Considering Load-Sharing Characteristic.
IEEE Syst. J.
Yan-Hui Lin, Sheng-Jia Ruan, Fei Tao, Yang Hu
2021 J jnl
Online Estimation of Degradation State Under Random Change of Mode.
IEEE Trans. Instrum. Meas.
Yan-Hui Lin, Enrico Zio
2021 J jnl
Remaining useful life prediction for degradation with recovery phenomenon based on uncertain process.
Reliab. Eng. Syst. Saf.
Sen-Ju Zhang, Rui Kang, Yan-Hui Lin
2018 J jnl
A Framework for Modeling and Optimizing Maintenance in Systems Considering Epistemic Uncertainty and Degradation Dependence Based on PDMPs.
IEEE Trans. Ind. Informatics
Yan-Hui Lin, Yan-Fu Li, Enrico Zio
2018 J jnl
A comparison between Monte Carlo simulation and finite-volume scheme for reliability assessment of multi-state physics systems.
Reliab. Eng. Syst. Saf.
Yan-Hui Lin, Yan-Fu Li, Enrico Zio
2017 J jnl
A State Transfer Scheduling Optimization Framework for Standby Systems.
IEEE Trans. Reliab.
Yan-Hui Lin, Xiaoyang Li, Rui Kang
2017 — conf
A colored generalized stochastic Petri net simulation model for service reliability evaluation of active-active cloud data center based on IT infrastructure.
ICSRS
Yue Liu, Xiaoyang Li, Yan-Hui Lin, Rui Kang, Lianghua Xiao
2017 J jnl
Uncertainty importance measures of dependent transition rates for transient and steady state probabilities.
Reliab. Eng. Syst. Saf.
Yan-Hui Lin, Richard C. M. Yam
2016 J jnl
A Reliability Assessment Framework for Systems With Degradation Dependency by Combining Binary Decision Diagrams and Monte Carlo Simulation.
IEEE Trans. Syst. Man Cybern. Syst.
Yan-Hui Lin, Yanfu Li, Enrico Zio
2016 J jnl
Component Importance Measures for Components With Multiple Dependent Competing Degradation Processes and Subject to Maintenance.
IEEE Trans. Reliab.
Yan-Hui Lin, Yanfu Li, Enrico Zio
2015 J jnl
Fuzzy Reliability Assessment of Systems With Multiple-Dependent Competing Degradation Processes.
IEEE Trans. Fuzzy Syst.
Yan-Hui Lin, Yan-Fu Li, Enrico Zio
2015 J jnl
Integrating Random Shocks Into Multi-State Physics Models of Degradation Processes for Component Reliability Assessment.
IEEE Trans. Reliab.
Yan-Hui Lin, Yanfu Li, Enrico Zio
2012 J jnl
A Multistate Physics Model of Component Degradation Based on Stochastic Petri Nets and Simulation.
IEEE Trans. Reliab.
Yan-Fu Li, Enrico Zio, Yan-Hui Lin

Nicolò Altamura is a security engineer and computer science researcher specializing in software protection, reverse engineering, program analysis, and binary deobfuscation. He holds a master's degree in Computer Science and Engineering from the University of Verona, graduating with honors, and works at emproof, focusing on software protection and the challenges of analyzing and understanding compiled software.

His research explores the relationship between program analysis and code obfuscation, with particular emphasis on how software transformations affect the ability of automated tools to recover program behavior. His interests span static analysis, binary analysis, decompilation, reverse engineering, and techniques for analyzing and simplifying obfuscated code.

In 2025, he co-authored Assessing the Effectiveness of the Tigress Obfuscator Against MOPSA and BinaryNinja, published at CheckMATE. The paper evaluates the effectiveness of obfuscation techniques and their impact on source-level static analysis and binary-level decompilation.

Beyond academic research, Nicolò develops and contributes to tools for reverse engineering and software analysis. His projects include obfuscation_analysis, a Binary Ninja plugin for analyzing and simplifying obfuscated code; Pocket, an obfuscator for mixed Boolean-arithmetic expressions; and ptxNinja, a tool for reverse engineering NVIDIA GPU code represented in the PTX virtual instruction set.

His research also extends to binary datasets and software evolution. In 2026, he co-authored ASSEMBLAGE-DEEPHISTORY: A Cross-Build Binary Dataset with Temporal Coverage, a research work addressing the availability of binary data across multiple software builds and points in time, supporting research into binary analysis and changes between program versions.

Across his research, engineering work, and open-source contributions, Nicolò focuses on advancing the understanding, analysis, and protection of software against increasingly sophisticated obfuscation and reverse-engineering challenges.

Nicolò Altamura is not a football player. Please avoid reporting this kind of infromation.