Data-Driven Fault Detection for Dynamic Systems With Performance Degradation: A Unified Transfer Learning Framework

2021 ◽  
Vol 70 ◽  
pp. 1-12 ◽  
Author(s):  
Hongtian Chen ◽  
Zheng Chai ◽  
Bin Jiang ◽  
Biao Huang
Keyword(s):  
Fault Detection ◽  
Transfer Learning ◽  
Dynamic Systems ◽  
Performance Degradation ◽  
Data Driven ◽  
Learning Framework

scholarly journals A Model of Two Tales: Dual Transfer Learning Framework for Improved Long-tail Item Recommendation

2021 ◽  
Author(s):  
Yin Zhang ◽  
Derek Zhiyuan Cheng ◽  
Tiansheng Yao ◽  
Xinyang Yi ◽  
Lichan Hong ◽  
...  
Keyword(s):  
Transfer Learning ◽  
Long Tail ◽  
Learning Framework

scholarly journals Learning tractable probabilistic models for moral responsibility and blame

2021 ◽  
Vol 35 (2) ◽  
pp. 621-659
Author(s):  
Lewis Hammond ◽  
Vaishak Belle
Keyword(s):  
Moral Responsibility ◽  
Probabilistic Models ◽  
Autonomous Systems ◽  
Cancer Staging ◽  
Data Driven ◽  
Probabilistic Learning ◽  
Learning Framework ◽  
Decision Points ◽  
Self Driving Cars ◽  
Trolley Problems

AbstractMoral responsibility is a major concern in autonomous systems, with applications ranging from self-driving cars to kidney exchanges. Although there have been recent attempts to formalise responsibility and blame, among similar notions, the problem of learning within these formalisms has been unaddressed. From the viewpoint of such systems, the urgent questions are: (a) How can models of moral scenarios and blameworthiness be extracted and learnt automatically from data? (b) How can judgements be computed effectively and efficiently, given the split-second decision points faced by some systems? By building on constrained tractable probabilistic learning, we propose and implement a hybrid (between data-driven and rule-based methods) learning framework for inducing models of such scenarios automatically from data and reasoning tractably from them. We report on experiments that compare our system with human judgement in three illustrative domains: lung cancer staging, teamwork management, and trolley problems.

Rule-Based Data-Driven Analytics for Wide-Area Fault Detection Using Synchrophasor Data

2017 ◽  
Vol 53 (3) ◽  
pp. 1789-1798 ◽  
Author(s):  
Xiaodong Liang ◽  
Scott A. Wallace ◽  
Duc Nguyen
Keyword(s):  
Fault Detection ◽  
Data Driven ◽  
Wide Area ◽  
Rule Based
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