Jin Wang spends much of her time researching Evidential reasoning approach, Fuzzy logic, Artificial intelligence, Bayesian network and Fuzzy set. Her Fuzzy logic research includes themes of Risk analysis and Reliability engineering. Her research investigates the connection between Artificial intelligence and topics such as Machine learning that intersect with issues in Probabilistic logic and Identification.
Her Bayesian network study integrates concerns from other disciplines, such as Risk analysis, Bayes' theorem, Estimation, Civil engineering and Operations research. Her study looks at the relationship between Fuzzy set and fields such as Data mining, as well as how they intersect with chemical problems. The Inference study combines topics in areas such as Belief structure and Rule-based system.
Jin Wang mainly focuses on Risk analysis, Evidential reasoning approach, Operations research, Risk assessment and Fuzzy logic. Jin Wang studies Risk analysis, namely Risk analysis. Among her Evidential reasoning approach studies, you can observe a synthesis of other disciplines of science such as Artificial intelligence, Reliability engineering, Machine learning, Inference and Fuzzy rule.
Her Operations research research is multidisciplinary, incorporating elements of Selection, Decision support system, Port, Bayesian network and Process. She is interested in Fuzzy set, which is a branch of Fuzzy logic. Her biological study spans a wide range of topics, including Analytic hierarchy process and Data mining.
Offshore wind power, Marine engineering, Bayesian network, Structural engineering and Collision are her primary areas of study. Her studies deal with areas such as Tower and Multiple attribute as well as Offshore wind power. Her Marine engineering research is multidisciplinary, relying on both Software and Deck.
Her work on Bending moment, Foundation and Seismic analysis as part of general Structural engineering study is frequently linked to Displacement, therefore connecting diverse disciplines of science. Her work in Identification addresses issues such as Flexibility, which are connected to fields such as Risk analysis. Her research in Risk analysis intersects with topics in TOPSIS, Decision support system and Operational efficiency.
Jin Wang focuses on Bayesian network, Structural engineering, Offshore wind power, Forensic engineering and Marine engineering. Jin Wang frequently studies issues relating to Accident analysis and Bayesian network. Her Seismic analysis, Foundation, Bending moment and Design load study in the realm of Structural engineering connects with subjects such as Multi body.
Her Seismic analysis study incorporates themes from Wind power and Aerodynamics. Her work carried out in the field of Offshore wind power brings together such families of science as Tower, Servo, Mooring, Spar and Nonlinear system. The various areas that Jin Wang examines in her Marine engineering study include Software, Risk management, Traffic simulation and Linear stability.
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MODIFIED FAILURE MODE AND EFFECTS ANALYSIS USING APPROXIMATE REASONING
Anand Pillay;Jin Wang.
Belief rule-base inference methodology using the evidential reasoning Approach-RIMER
Jian-Bo Yang;Jun Liu;Jin Wang;How-Sing Sii.
Automatic Identification System (AIS) : data reliability and human error implications
Abbas Harati-Mokhtari;Alan Wall;Philip Brooks;Jin Wang.
Fuzzy Rule-Based Bayesian Reasoning Approach for Prioritization of Failures in FMEA
Zaili Yang;S. Bonsall;Jin Wang.
Inference and learning methodology of belief-rule-based expert system for pipeline leak detection
Dong Ling Xu;Jun Liu;Jian Bo Yang;Guo Ping Liu;Guo Ping Liu;Guo Ping Liu.
Safety analysis and synthesis using fuzzy sets and evidential reasoning
J. Wang;J.B. Yang;P. Sen.
Incorporation of formal safety assessment and Bayesian network in navigational risk estimation of the Yangtze River
Di Zhang;Xinping Yan;Zaili Yang;Alan D. Wall.
Formal safety assessment of cruise ships
P Lois;J Wang;A Wall;T Ruxton.
A fuzzy-logic-based approach to qualitative safety modelling for marine systems
How Sing Sii;Tom Ruxton;Jin Wang.
The use of Bayesian network modelling for maintenance planning in a manufacturing industry
B. Jones;Ian Jenkinson;Zaili Yang;Jin Wang.
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