No abstract

Since the dynamical system and control system of the missile are typically nonlinear, an effective acceleration tracking autopilot is designed using the dynamic surface control (DSC) technique in order to make the missile control system more robust despite the uncertainty of the dynamical parameters and the presence of disturbances. Firstly, the nonlinear mathematical model of the tail-controlled missile is decomposed into slow acceleration dynamics and fast pitch rate dynamics based on the naturally existing time scale separation. Secondly, the controller based on DSC is designed after obtaining the linear dynamics characteristics of the slow and fast subsystems. An extended state observer is used to detect the uncertainty of the system state variables and aerodynamic parameters to achieve the compensation of the control law. The closed-loop stability of the controller is derived and rigorously analyzed. Finally, the effectiveness and robustness of the design is verified by Monte Carlo simulation considering different initial conditions and parameter uptake. Simulation results illustrate that the missile autopilot based DSC controller achieves better performance and robustness than the other two well-known autopilots. The method proposed in this paper is applied to the design of a missile autopilot, and the results show that the acceleration tracking autopilot based on the DSC controller can ensure accurate tracking of the required commands and has better performance.

<abstract><p>The existing classification methods of LiDAR point cloud are almost based on the assumption that each class is balanced, without considering the imbalanced class problem. Moreover, from the perspective of data volume, the LiDAR point cloud classification should be a typical big data classification problem. Therefore, by studying the existing deep network structure and imbalanced sampling methods, this paper proposes an oversampling method based on stack autoencoder. The method realizes automatic generation of synthetic samples by learning the distribution characteristics of the positive class, which solves the problem of imbalance training data well. It only takes the geometric coordinates and intensity information of the point clouds as the input layer and does not need feature construction or fusion, which reduces the computational complexity. This paper also discusses the influence of sampling number, oversampling method and classifier on the classification results, and evaluates the performance from three aspects: true positive rate, positive predictive value and accuracy. The results show that the oversampling method based on stack autoencoder is suitable for imbalanced LiDAR point cloud classification, and has a good ability to improve the effect of positive class. If it is combined with optimized classifier, the classification performance of imbalanced point cloud is greatly improved.</p></abstract>

In modern world, most of the optimization problems are nonconvex which are neither convex nor concave. The objective of this research is to study a class of nonconvex functions, namely, strongly nonconvex functions. We establish inequalities of Hermite-Hadamard and Fejér type for strongly nonconvex functions in generalized sense. Moreover, we establish some fractional integral inequalities for strongly nonconvex functions in generalized sense in the setting of Riemann-Liouville integral operators.

Neutrosophic logic is frequently applied to the engineering technology, scientific administration, and financial matters, among other fields. In addition, neutrosophic linear systems can be used to illustrate various practical problems. Due to the complexity of neutrosophic operators, however, solving linear neutrosophic systems is challenging. This work proposes a new straightforward method for solving the neutrosophic system of linear equations based on the neutrosophic structured element (NSE). Here unknown and right-hand side vectors are considered as triangular neutrosophic numbers. Based on the NSE, analytical expressions of the solution to this equation and its degrees are also provided. Finally, several examples of the methodology are provided.