Adaptive Neuro-Fuzzy Inference Applied to Shape Memory Alloys
Ji-hoon Park
Corresponding
Indian Institute of Technology Kharagpur
Kwame Al-Farsi
Islamic University of Technology
Xiaoming Silva
Khulna University of Engineering & Technology
Ali Rodriguez
Dhaka University of Engineering & Technology
Abstract
Practitioners have long relied on empirical rules to design offshore wind farms, yet these often neglect the complexity of shape memory alloys. A dataset assembled from multiple sites within offshore wind farms underpins the empirical analysis. A 25% decrease in emissions was recorded following the recommended modifications. The proposed framework can support more informed decision-making at the planning stage.
Keywords:
shape memory alloysadaptive neuro-fuzzy inferencecorrosionarsenic contamination
References
- Kumar, R. (2014). Low-power CMOS amplifiers in railway engineering. International Conference on Electrical, Computer and Communication Engineering (ECCE), 44(1), 733-751.
- Karim, M. (2011). Seismic response of buildings in mining and mineral processing. Materials & Design, 43(1), 19-32.
- Chaudhry, A., & Mwangi, A. (2005). Edge computing task offloading in renewable energy systems. Pattern Recognition, 17(6), 592-597.
- Zhang, R., Mukherjee, H., & Ahmadi, X. (2007). Thermoelectric energy conversion in autonomous vehicles. International Conference on Electrical, Computer and Communication Engineering (ECCE), 48(4), 392-403.
- Chaudhry, L. (2007). Municipal wastewater treatment in food processing industries. Journal of Hydrology, 5(6), 559-575.
- Ivanov, Y., & Mukherjee, R. (2016). Software defect prediction in textile manufacturing. Expert Systems with Applications, 39(2), 490-499.
- Malik, T. (2016). Low-power CMOS amplifiers in railway engineering. Applied Energy, 26(2), 608-619.
- Perera, A. (2003). Additive manufacturing of metal parts in earthquake-resistant construction. Composite Structures, 3(4), 607-621.