The inaccuracy of the multi-fins synergy hydrodynamic model of the robotic fish and the lack of clarity between the control parameters of the locomotion gait and swimming behavior of the robotic fish were addressed. W...
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The inaccuracy of the multi-fins synergy hydrodynamic model of the robotic fish and the lack of clarity between the control parameters of the locomotion gait and swimming behavior of the robotic fish were addressed. We constructed a bionic robotic fish pectoral fins and flexible body synergy motion gait model by using a Central Pattern Generator (CPG) network. We obtained the rowing phase difference, pectoral fins rotational phase difference, flexible body fluctuation bias, frequency, and the velocity and positional attitude datasets of fish through numerical simulations using Computational Fluid Dynamics (CFD). We proposed identification and control models based on the least squares support vector machine regression (LSSVR) interactive network and constructed hydrodynamic models of pectoral fins, flexible body parameters, and locomotion modes using the identification model. The identification model data were used to design the control model offline control law, and the identification and control model parameters were updated online by combining them with the experimental dataset. The experimental results indicated that the displacement REMS were all less than 0.1, and the maximum error of the spatial motion trajectory was 0.08 m. The identification and control models enabled the robotic fish to accurately track the desired trajectory.
With the rapid economic development in rural areas of our country, the scale of the rural distribution network is ex-panding constantly. However, the existing rural distribution network still has problems, such as unr...
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作者：
Du, RuizhongZhen, LinHebei Univ
Sch Cyberspace Secur & Comp Sci Baoding 071002 Hebei Peoples R China Hebei Univ
Hebei Key Lab Highly Trusted Informat Syst Baoding 071002 Peoples R China
YWireless system in industrial scene plays an important role in the process of automation. This kind of system urgently needs low complexity, lightweight, high security authentication mechanism. The emergence of physi...
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YWireless system in industrial scene plays an important role in the process of automation. This kind of system urgently needs low complexity, lightweight, high security authentication mechanism. The emergence of physical layer authentication meets these requirements. However, the existing authentication mechanism based on binary hypothesis testing can only perform ideally under fixed conditions, and cannot distinguish multiple users;The authentication mechanism based on deep neural network (DNN) algorithm has limitations in small sample learning and parameter setting. In order to further improve the accuracy of authentication in dynamic industrial scenarios, a new multiuser physical layer authentication scheme is proposed. The mechanism uses machine learning algorithm based on autonomous parameteroptimization to replace the traditional decision making method based on user-defined threshold, and is suitable for small sample learning. This paper takes the channel matrix estimated by the mobile node as the authentication input, obtains different channel matrix dimensions through down sampling, and finds out the optimal channel matrix dimension through experiments, so as to reduce the running time and improve the authentication accuracy. A large number of simulations are carried out using the public dynamic industrial scene data set. Compared with the existing authentication schemes, the proposed authentication scheme further improves the accuracy of multiuser authentication in dynamic industrial scenarios. (C) 2021 Elsevier Ltd. All rights reserved.
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