Raju Sekar Kidambi, Varadarajan Ganesh Karthikeyan, Alharbi Amal H, Kannan Subhash, Khafaga Doaa Sami, Sundaramoorthy Raj Anand, Eid Marwa M, Towfek S K
School of Computing, SASTRA Deemed University, Thanjavur, 613401, India.
Department of Computer Sciences, College of Computer and Information Sciences, Princess Nourah Bint Abdulrahman University, P.O. Box 84428, 11671, Riyadh, Saudi Arabia.
Sci Rep. 2024 Oct 14;14(1):24073. doi: 10.1038/s41598-024-72194-5.
Energy harvesters based on nanomaterials are getting more and more popular, but on their way to commercial availability, some crucial issues still need to be solved. The objective of the study is to select an appropriate nanomaterial. Using features of the Reinforcement Deep Q-Network (DQN) in conjunction with Fuzzy PROMETHEE, the proposed model, we present in this work a hybrid fuzzy approach to selecting appropriate materials for a vehicle-environmental-hazardous substance (EHS) combination that operates in roadways and under traffic conditions. The DQN is able to accumulate useful experience of operating in a dynamic traffic environment, accordingly selecting materials that deliver the highest energy output but at the same time bring consideration to factors such as durability, cost, and environmental impact. Fuzzy PROMETHEE allows the participation of human experts during the decision-making process, going beyond the quantitative data typically learned by DQN through the inclusion of qualitative preferences. Instead, this hybrid method unites the strength of individual approaches, as a result providing highly resistant and adjustable material selection to real EHS. The result of the study pointed out materials that can give high energy efficiency with reference to years of service, price, and environmental effects. The proposed model provides 95% accuracy with a computational efficiency of 300 s, and the application of hypothesis and practical testing on the chosen materials showed the high efficiency of the selected materials to harvest energy under fluctuating traffic conditions and proved the concept of a hybrid approach in True Vehicle Environmental High-risk Substance scenarios.
基于纳米材料的能量收集器越来越受欢迎，但在其实现商业应用的道路上，仍有一些关键问题需要解决。本研究的目的是选择一种合适的纳米材料。结合强化深度Q网络（DQN）和模糊偏好顺序结构评估法（Fuzzy PROMETHEE）的特点，我们在这项工作中提出了一种混合模糊方法，用于为在道路和交通条件下运行的车辆-环境-有害物质（EHS）组合选择合适的材料。DQN能够积累在动态交通环境中运行的有用经验，从而选择能提供最高能量输出的材料，同时兼顾耐久性、成本和环境影响等因素。模糊偏好顺序结构评估法允许人类专家参与决策过程，通过纳入定性偏好，超越了DQN通常学习的定量数据。相反，这种混合方法结合了各个方法的优势，从而为实际的EHS提供了高度抗性和可调整的材料选择。研究结果指出了在使用寿命、价格和环境影响方面能提供高能效的材料。所提出的模型准确率达到95%，计算效率为300秒，对所选材料进行的假设和实际测试表明，所选材料在波动的交通条件下收集能量的效率很高，并证明了在真实车辆环境高风险物质场景中采用混合方法的概念。
