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| Model-driven channel modeling approach in laminar channel |
| WANG Yue1, BAO Xu1, LIN Feng2 |
| 1. School of Computer Science and Communication Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, China; 2. Key Laboratory of Healthy Freshwater Aquaculture of Ministry of Agriculture, Zhejiang Institute of Freshwater Fisheries, Huzhou, Zhejiang 313001, China |
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Abstract To solve the problem that the existing laminar channel models could not be directly applied to the complex laminar channels with targets, a model-driven channel modeling approach was proposed. The system model for laminar diffusion channels with targets was extended by incorporating additional parameters in the absence of target advection models, and the effects of laminar flow and targets on received molecules were considered. Based on the simulation results, the complex laminar channel with target was approximated as two steady laminar channels, and the point-source-receiver laminar diffusion channel model with considering the presence of target was established. The Levenberg-Marquardt algorithm was employed to learn and predict channel model parameters by the neural network, and the combination of data and model driven (CDMD) detection method was proposed for target identification. The results show that the accuracy of the channel model can be validated through the comparison of formula data and simulated data with correlation coefficient of 0.999 15, which confirms the feasibility of the neural network model. The proposed target detection method can be verified by the binary classification algorithm within the neural network with detection accuracy rate of 98.8%. The CDMD-based detection method requires approximately one-sixth of the data volume needed for data-driven detection methods for maintaining high detection performance.
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Received: 27 October 2022
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2024, Vol. 45 
