Query：
学者姓名：李方昱
Refining：
Year
Type
Indexed by
Source
Complex
Co-Author
Language
Clean All
Abstract ：
Multiple sensors are widely used in the monitoring of the degradation process and prediction of the remaining useful lifetime (RUL) of units in complex engineering systems. However, ensuring the prognostic performance with only a few units available remains difficult. Under a few-shot scenario, the discordant observations that exist in sensor data introduce considerable uncertainty into the degradation model, which leads to an empirical loss far from the expected loss. On the other hand, the learned degradation model tends to be overfitted on the limited available units and results in a biased model parameter distribution, which limits the model generalization capability on unseen units. To address these issues, this paper proposes a restricted-learning network with observation credibility inference (OCI) for few-shot degradation modeling. We initially introduce the OCI to figure out discordant observations from sensor data. Then, OCI is incorporated into restrictive learning through the deletion of discordant observations from sensor data, which enforces a prior distribution constraint on degradation model parameters to prevent overfitting. Finally, a posterior augmented classifier is learned to estimate health status based on the posterior sensor paths, and the RUL can be predicted subsequently. A case study that uses the degradation dataset of aircraft engines demonstrates the superiority of the proposed method over benchmark methods under few-shot scenarios.
Keyword ：
degradation modeling degradation modeling restricted-learning network restricted-learning network RUL prediction RUL prediction Few-shot scenario Few-shot scenario discordant observations discordant observations
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Wang, Ying , Li, Fangyu , Wang, Di et al. A Restricted-Learning Network With Observation Credibility Inference for Few-Shot Degradation Modeling [J]. | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2025 , 22 : 10177-10192 . |
| MLA | Wang, Ying et al. "A Restricted-Learning Network With Observation Credibility Inference for Few-Shot Degradation Modeling" . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING 22 (2025) : 10177-10192 . |
| APA | Wang, Ying , Li, Fangyu , Wang, Di , Qin, Wei . A Restricted-Learning Network With Observation Credibility Inference for Few-Shot Degradation Modeling . | IEEE TRANSACTIONS ON AUTOMATION SCIENCE AND ENGINEERING , 2025 , 22 , 10177-10192 . |
| Export to | NoteExpress RIS BibTex |
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Fangyu , Song, WenZhan , Xu, Xiaohua . Editorial: Special Issue on Cyber-Physical Security and Zero Trust [J]. | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
| MLA | Li, Fangyu et al. "Editorial: Special Issue on Cyber-Physical Security and Zero Trust" . | ACM TRANSACTIONS ON SENSOR NETWORKS 20 . 2 (2024) . |
| APA | Li, Fangyu , Song, WenZhan , Xu, Xiaohua . Editorial: Special Issue on Cyber-Physical Security and Zero Trust . | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Data privacy has become a critical concern in today's data-driven world. Interval privacy emerges as a promising safeguard, representing private values as intervals. Traditional interval analysis methods, however, often rely on critical assumptions that are questionable in practice. To address this gap, we propose a novel paradigm for analyzing interval-valued data generated by the interval privacy mechanism. Our contributions are two-fold: First, we innovatively model intervals as random objects in a metric space and use the Hausdorff distance to quantify their dissimilarity without imposing restrictive assumptions. Second, as an application of our paradigm, we develop an interval-to-interval regression method named Hausdorff distance-based regression (HDBR), extending multivariate linear regression to metric spaces. The HDBR method estimates regression coefficients by minimizing the Hausdorff distance between the observed and estimated intervals. Simulation studies demonstrate the effectiveness and robustness of our proposed approach compared to mainstream competitors. We also provide a real data example to illustrate how to perform regression analysis within the interval privacy framework, and the results further validate the superiority of the HDBR method.
Keyword ：
Synthetic data Synthetic data Upper bound Upper bound Data models Data models Data privacy Data privacy interval privacy interval privacy Predictive models Predictive models Hausdorff distance Hausdorff distance interval data interval data Extraterrestrial measurements Extraterrestrial measurements metric space metric space linear regression model linear regression model Linear regression Linear regression
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Kang, Xinlai , Li, Mengyu , Chen, Xuqiang et al. A Hausdorff Regression Paradigm for Interval Privacy [J]. | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 : 146-150 . |
| MLA | Kang, Xinlai et al. "A Hausdorff Regression Paradigm for Interval Privacy" . | IEEE SIGNAL PROCESSING LETTERS 31 (2024) : 146-150 . |
| APA | Kang, Xinlai , Li, Mengyu , Chen, Xuqiang , Li, Fangyu , Meng, Cheng . A Hausdorff Regression Paradigm for Interval Privacy . | IEEE SIGNAL PROCESSING LETTERS , 2024 , 31 , 146-150 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Cybersecurity breaches are common anomalies for distributed cyber-physical systems (CPS). However, the cyber security breach classification is still a difficult problem, even using cutting-edge artificial intelligence (AI) approaches. In this article, we study a multi-class classification problem in cyber security for attack detection. A challenging multi-node data-censoring case is considered. In such a case, data within each data center/node cannot be shared while the local data is incomplete. Particularly, local nodes contain only a part of the multiple classes. In order to train a global multi-class classifier without sharing the raw data across all nodes, we design a multi-node multi-class classification ensemble approach which is the main result of our study. By gathering the estimated parameters of the binary classifiers and data densities from each local node, the missing information for each local node is completed to build the global multi-class classifier. Numerical experiments are given to validate the effectiveness of the proposed approach under the multi-node datacensoring case. Under such a case, we even show the out-performance of the proposed approach over the full-data approach.
Keyword ：
multi-class classification multi-class classification ensemble learning ensemble learning cyber security cyber security Federated learning Federated learning
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Liu, Junyi , Tang, Yifu , Zhao, Haimeng et al. CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach [J]. | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
| MLA | Liu, Junyi et al. "CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach" . | ACM TRANSACTIONS ON SENSOR NETWORKS 20 . 2 (2024) . |
| APA | Liu, Junyi , Tang, Yifu , Zhao, Haimeng , Wang, Xieheng , Li, Fangyu , Zhang, Jingyi . CPS Attack Detection under Limited Local Information in Cyber Security: An Ensemble Multi-Node Multi-Class Classification Approach . | ACM TRANSACTIONS ON SENSOR NETWORKS , 2024 , 20 (2) . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Missing values frequently appearing in the wastewater treatment process are automatically replaced by zero to ensure the implementation of downstream applications. These meaningless zero values bias data distribution and decrease data quality. However, the existing imputation methods treat all values equally without considering the existence of meaningless zero values, affecting the performances of imputation and downstream models. Thus, an attention-guided position-sensitive multiple imputation (APMI) method is proposed. First, a position-sensitive localization attention module selectively focuses on the most informative values, enhancing the ability for observed data utilization. Second, a masked attention multiple imputation module focuses on the observed values and fuses multiple candidate estimations as the final result to improve imputation performance. Third, a joint optimization objective function is designed to ensure the consistency of localization and imputation tasks. The extensive experimental results show that the proposed APMI outperforms existing method imputation performance under different missing rates.
Keyword ：
Imputation Imputation Matrix converters Matrix converters Joint optimization objective function Joint optimization objective function Data models Data models Decoding Decoding multiple imputation multiple imputation Attention mechanisms Attention mechanisms Optimization Optimization wastewater treatment process (WWTP) wastewater treatment process (WWTP) missing values missing values localization module localization module Location awareness Location awareness
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Sun, Meiting , Li, Fangyu , Han, Honggui . Attention-Guided Position-Sensitive Multiple Imputation for Wastewater Treatment Process [J]. | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) : 14459-14468 . |
| MLA | Sun, Meiting et al. "Attention-Guided Position-Sensitive Multiple Imputation for Wastewater Treatment Process" . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS 20 . 12 (2024) : 14459-14468 . |
| APA | Sun, Meiting , Li, Fangyu , Han, Honggui . Attention-Guided Position-Sensitive Multiple Imputation for Wastewater Treatment Process . | IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS , 2024 , 20 (12) , 14459-14468 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Unsupervised Domain Adaptation (UDA) has been widely used for fault diagnosis to solve data distribution shifts in multi-source domains. However, UDA ignores the imbalance of the class distribution in the domain, which makes it difficult to apply to real industrial processes. Therefore, we propose a class-aware multi-source domain adaptation (CMDA) for imbalanced fault diagnosis. Firstly, a batch interaction feature extractor is designed to apply the cross-attention mechanism to the batch dimension to capture nonlinear relationships among imbalanced features of classes. Secondly, we construct a class-aware UDA module that is introduced with class information in both the feature and discriminant layers to realize domain adaptation. The module performs multilinear augmentation of imbalanced class samples for data alignment. Finally, a task classifier is designed that combines multiple classifiers with reliability-weighted scores to weaken negative transfer and jointly accomplish fault diagnosis of rolling bearings. The reliability-weighted scores are determined by the inter-domain distance and diagnostic accuracy. We conducted experiments using the CWRU dataset under 4 protocols and 12 scenarios and verified the effectiveness of CMDA.
Keyword ：
Domain shift Domain shift Rolling bearing Rolling bearing Unsupervised domain adaptation Unsupervised domain adaptation Fault diagnosis Fault diagnosis Imbalanced class Imbalanced class
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Gao, Huihui , Xue, Zihan , Han, Honggui et al. Class-Aware Multi-Source Domain Adaptation for Imbalanced Fault Diagnosis [J]. | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 : 1530-1535 . |
| MLA | Gao, Huihui et al. "Class-Aware Multi-Source Domain Adaptation for Imbalanced Fault Diagnosis" . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 (2024) : 1530-1535 . |
| APA | Gao, Huihui , Xue, Zihan , Han, Honggui , Li, Fangyu . Class-Aware Multi-Source Domain Adaptation for Imbalanced Fault Diagnosis . | 2024 14TH ASIAN CONTROL CONFERENCE, ASCC 2024 , 2024 , 1530-1535 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
The construction industry is currently going through an intelligent revolution. The profound transformation of the Industry 4.0 era is made possible by contemporary technologies such as Internet of Things (IoT), cloud computing, and robotics. Essentially, the vast amount of diverse big data from many sources should be properly utilized to enhance the entire life-cycle construction process. Construction efficiency can be enhanced while material waste and construction expenses are reduced, planning and decision-making processes can be improved while errors are lowered, and applications of big data in construction analytics will make construction sites safer. This article not only offers a comprehensive review of the advantages of associated big data approaches, but it also assesses the current state of the art in the construction industry. Several unresolved difficulties are also discussed. In the end, we express our thoughts on the potential future of big data in the construction industry.
Keyword ：
Data analytics Data analytics Big data Big data Construction Construction Data engineering Data engineering
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Fangyu , Laili, Yuanjun , Chen, Xuqiang et al. Towards big data driven construction industry [J]. | JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION , 2023 , 35 . |
| MLA | Li, Fangyu et al. "Towards big data driven construction industry" . | JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION 35 (2023) . |
| APA | Li, Fangyu , Laili, Yuanjun , Chen, Xuqiang , Lou, Yihuai , Wang, Chen , Yang, Hongyan et al. Towards big data driven construction industry . | JOURNAL OF INDUSTRIAL INFORMATION INTEGRATION , 2023 , 35 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Recently, the computation of seismic fault attribute that may be significant in seismic interpretation is that seismic fault detection is treated as an image segmentation problem using different deep-learning (DL) archi-tectures. To do this, researchers have concentrated on applying cutting-edge DL architectures in computing seismic fault attributes. To explore the factors that may affect the accuracy of seismic fault attribute, we com-pare the computed fault probability using DL architectures under different scenarios. The designed scenarios aim to highlight the leading factors that may affect the accuracy and resolution of seismic image segmentation. The discussed factors include the dimension and size of training data, training data preparation, ensemble learn-ing, and batch size in DL. The proposed comparisons are applied to one marine seismic survey from New Zealand and one land seismic survey from China. The results demonstrate that properly preparing training data is far more important than choosing a cutting-edge DL architecture in computing seismic fault attribute. We also propose a practical workflow that can include real seismic data and corresponding interpreted fault sticks in training data for a specific seismic survey.
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Zhang, Bo , Pu, Yitao , Xu, Zhaohui et al. Exploring factors affecting the performance of deep learning in seismic fault attribute computation [J]. | INTERPRETATION-A JOURNAL OF SUBSURFACE CHARACTERIZATION , 2022 , 10 (4) : T619-T636 . |
| MLA | Zhang, Bo et al. "Exploring factors affecting the performance of deep learning in seismic fault attribute computation" . | INTERPRETATION-A JOURNAL OF SUBSURFACE CHARACTERIZATION 10 . 4 (2022) : T619-T636 . |
| APA | Zhang, Bo , Pu, Yitao , Xu, Zhaohui , Liu, Naihao , Li, Shizhen , Li, Fangyu . Exploring factors affecting the performance of deep learning in seismic fault attribute computation . | INTERPRETATION-A JOURNAL OF SUBSURFACE CHARACTERIZATION , 2022 , 10 (4) , T619-T636 . |
| Export to | NoteExpress RIS BibTex |
Abstract ：
Development of a cyber security strategy for the active distribution systems is challenging due to the inclusion of distributed renewable energy generations. This paper proposes an adaptive hierarchical cyber attack detection and localization framework for distributed active distribution systems via analyzing electrical waveforms. Cyber attack detection is based on a sequential deep learning model, via which even minor cyber attacks can be identified. The two-stage cyber attack localization algorithm first estimates the cyber attack sub-region, and then localize the specified cyber attack within the estimated sub-region. We propose a modified spectral clustering-based network partitioning method for the hierarchical cyber attack 'coarse' localization. Next, to further narrow down the cyber attack location, a normalized impact score based on waveform statistical metrics is proposed to obtain a 'fine' cyber attack location by characterizing different waveform properties. Finally, compared with classical and state-of-art methods, a comprehensive quantitative evaluation with two case studies shows promising estimation results of the proposed framework.
Keyword ：
Adaptive systems Adaptive systems adaptive adaptive Topology Topology Sensors Sensors Monitoring Monitoring hierarchical hierarchical distribution networks distribution networks Cyberattack Cyberattack Adaptation models Adaptation models online online Location awareness Location awareness Cyber attack localization Cyber attack localization
Cite：
Copy from the list or Export to your reference management。
| GB/T 7714 | Li, Qi , Zhang, Jinan , Zhao, Junbo et al. Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems [J]. | IEEE TRANSACTIONS ON SMART GRID , 2022 , 13 (3) : 2369-2380 . |
| MLA | Li, Qi et al. "Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems" . | IEEE TRANSACTIONS ON SMART GRID 13 . 3 (2022) : 2369-2380 . |
| APA | Li, Qi , Zhang, Jinan , Zhao, Junbo , Ye, Jin , Song, Wenzhan , Li, Fangyu . Adaptive Hierarchical Cyber Attack Detection and Localization in Active Distribution Systems . | IEEE TRANSACTIONS ON SMART GRID , 2022 , 13 (3) , 2369-2380 . |
| Export to | NoteExpress RIS BibTex |
Export
| Results： |
Selected to |
| Format： |
