Seismic hazard in East Africa: An example of the application of incomplete and uncertain data

1991 ◽  
Vol 4 (4) ◽  
pp. 421-430 ◽  
Author(s):  
P�ivi M�ntyniemi ◽  
Andrzej Kijko
Keyword(s):  
Seismic Hazard ◽  
East Africa ◽  
Uncertain Data ◽  
Incomplete And Uncertain Data

scholarly journals A bad data detection approach to EPS state estimation based on fuzzy sets and wavelet analysis

2020 ◽  
Vol 216 ◽  
pp. 01029
Author(s):  
Irina Kolosok ◽  
Liudmila Gurina
Keyword(s):  
Wavelet Analysis ◽  
Fuzzy Sets ◽  
State Estimation ◽  
Uncertain Data ◽  
Data Detection ◽  
Bad Data Detection ◽  
Combined Application ◽  
Measurement Validation ◽  
Incomplete And Uncertain Data ◽  
Bad Data

The paper offers an algorithm for detection of erroneous measurements (bad data) that occur at cyberattacks against systems for data acquisition, processing and transfer and cannot be detected by conventional methods of measurement validation at EPS state estimation. Combined application of wavelet analysis and theory of fuzzy sets when processing the SCADA and WAMS measurements produces higher accuracy of the estimates obtained under incomplete and uncertain data and demonstrates the efficiency of proposed approach for practical computations in simulated cyberattacks.

scholarly journals Application of the Structure Function in the Evaluation of the Human Factor in Healthcare

Symmetry ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 93
Author(s):  
Elena Zaitseva ◽  
Vitaly Levashenko ◽  
Jan Rabcan ◽  
Emil Krsak
Keyword(s):  
Structure Function ◽  
Medical Error ◽  
System Performance ◽  
Human Factor ◽  
Uncertain Data ◽  
Human Reliability Analysis ◽  
Performance Levels ◽  
New Device ◽  
Incomplete And Uncertain Data ◽  
One Year

A structure function is one of the possible mathematical models of systems in reliability engineering. A structure function maps sets of component states into system performance levels. Methods of system reliability evaluation based on structure function representation are well established. A structure function can be formed based on completely specified data about system behavior. Such data for most real-world systems are incomplete and uncertain. The typical example is analysis and evaluation of the human factor. Therefore, the structure function is not used in human reliability analysis (HRA) typically. In this paper, a method for structure function construction is proposed based on incomplete and uncertain data in HRA. The proposed method application is considered for healthcare to evaluate medical error. This method is developed using a fuzzy decision tree (FDT), which allows all possible component states to be classified into classes of system performance levels. The structure function is constructed based on the decision table, which is formed according to the FDT. A case study for this method is considered by evaluating the human factor in healthcare: complications in the familiarization and exploitation of a new device in a hospital department are analyzed and evaluated. This evaluation shows the decreasing of medical errors in diagnosis after one year of device exploitation and a slight decrease in quality of diagnosis after two months of device exploitation. Numerical values of probabilities of medical error are calculated based on the proposed approach.

scholarly journals Automated Reasoning for City Infrastructure Maintenance Decision Support

2018 ◽  
Author(s):  
Lijun Wei ◽  
Derek R. Magee ◽  
Vania Dimitrova ◽  
Barry Clarke ◽  
Heshan Du ◽  
...  
Keyword(s):  
Decision Support ◽  
Asset Management ◽  
Uncertain Data ◽  
Data Retrieval ◽  
Case Scenario ◽  
Worst Case ◽  
Domain Experts ◽  
Incomplete And Uncertain Data ◽  
Infrastructure Maintenance ◽  
Parameter Values

We present an interactive decision support system for assisting city infrastructure inter-asset management. It combines real-time site specific data retrieval, a knowledge base co-created with domain experts and an inference engine capable of predicting potential consequences and risks resulting from the available data and knowledge. The system can give explanations of each consequence, cope with incomplete and uncertain data by making assumptions about what might be the worst case scenario, and making suggestions for further investigation. This demo presents multiple real-world scenarios, and demonstrates how modifying assumptions (parameter values) can lead to different consequences.

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