Projects per year
Fingerprint Dive into the research topics where School of Engineering, Industrial Engineering is active. These topic labels come from the works of this organization's members. Together they form a unique fingerprint.
Software reliability
Engineering & Materials Science
Costs
Engineering & Materials Science
Machining
Engineering & Materials Science
Degradation
Engineering & Materials Science
Testing
Engineering & Materials Science
Redundancy
Engineering & Materials Science
Reliability analysis
Engineering & Materials Science
Inspection
Engineering & Materials Science
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Profiles
Projects 2008 2019
- 7 Finished
UNCERTAINTY MANAGEMENT AND PROACTIVE MAINTENANCE FOR LITHIUM-ION BATTERIES IN ELECTRIFIED VEHICLES
National Science Foundation (National Science Foundation (NSF))
9/1/17 → 7/31/19
Project: Research project
Lithium-ion batteries
Uncertainty
Computational efficiency
Degradation
Equivalent circuits
COLLABORATIVE RESEARCH: PROCESS MONITORING AND CONTROL IN AUTOCORRELATED MULTISTAGE MANUFACTURING PROCESSES
National Science Foundation (National Science Foundation (NSF))
9/1/12 → 8/31/15
Project: Research project
Process monitoring
Process control
Monitoring
Industry
Chemical industry
EAGER: PULSED LASER ASSISTED EXFOLIATION OF SINGLE CRYSTALLINE SIC THIN LAYERS FOR COST EFFECTIVE MICRO-DEVICE FABRICATION
National Science Foundation (National Science Foundation (NSF))
9/1/11 → 8/31/12
Project: Research project
structural influence coefficients
silicon carbides
pulsed lasers
costs
fabrication
Research Output 1973 2020
1
Citation
(Scopus)
A novel generalized logistic dependent model to predict the presence of breast cancer based on biomarkers
Pham, H. & Pham, D. H., Jan 10 2020, In : Concurrency Computation. 32, 1, e5467.Research output: Contribution to journal › Article
Biomarkers
Breast Cancer
Logistics
Predict
Dependent
Convergence of deep machine learning and parallel computing environment for bio-engineering applications
Sangaiah, A. K., Pham, H., Qiu, T. & Muhammad, K., Jan 10 2020, In : Concurrency Computation. 32, 1, e5424.Research output: Contribution to journal › Editorial
Parallel processing systems
Parallel Computing
Engineering Application
Learning systems
Machine Learning
Generalized support vector data description for anomaly detection
Turkoz, M., Kim, S., Son, Y., Jeong, M. K. & Elsayed, E. A., Apr 2020, In : Pattern Recognition. 100, 107119.Research output: Contribution to journal › Article
Data description