Recent work has shown that the activation function of the convolutional neural network can meet the Lipschitz condition, then the corresponding convolutional neural network structure can be constructed according to th...
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This paper investigates the design and detection problems of stealthy false data injection (FDI) attacks against networked control systems from the different perspectives of an attacker and a defender, respectively. F...
This paper investigates the design and detection problems of stealthy false data injection (FDI) attacks against networked control systems from the different perspectives of an attacker and a defender, respectively. First, a Kalman filter-based output tracking control system is presented, where stealthy FDI attacks are designed for its feedback and forward channels so as to destroy the system performance while bypassing a traditional residual-based detector. Second, to successfully detect such two-channel stealthy attacks, an active data modification scheme is proposed, by which the measurement and control data are amended before transmitting them through communication networks. Theoretical analysis is then carried out for both ideal and practical cases to evaluate the effectiveness of the detection scheme. An interesting finding is that the attacks designed based on a false model obtained from those modified data can remain stealthy. Finally, simulation results are provided to validate the proposed attack design and detection schemes.
Effective postprandial glucose control is important to glucose management for subjects with diabetes mellitus. In this work, a data-driven meal bolus decision method is proposed without the need of subject-specific gl...
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A nonlinear robust trajectory tracking strategy for a gliding hypersonic vehicle with an aileron stuck at an unknown position is presented in this paper. First, the components of translational motion dynamics perpendi...
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A nonlinear robust trajectory tracking strategy for a gliding hypersonic vehicle with an aileron stuck at an unknown position is presented in this paper. First, the components of translational motion dynamics perpendicular to the velocity are derived, and then a guidance law based on a time-varying sliding mode method is used to realize trajectory tracking. Furthermore, the rotational equations of motion are separated into an actuated subsystem and an unactuated subsystem. And an adaptive time-varying sliding mode attitude controller is proposed based on the actuated subsystem to track the command attitude and the tracking performance and robustness are therefore enhanced. The proposed guidance law and attitude controller make the hypersonic vehicle fly along the reference trajectory even when the aileron is stuck at an unknown angle. Finally, a hypersonic benchmark platform is used to demonstrate the effectiveness of the proposed strategy.
Preventing and controlling Low-Slow-Small(LSS) targets are problems to be solved due to their good concealment in urban environment. Consequently, the problem of target assignment is studied in this paper. Firstly, a ...
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Preventing and controlling Low-Slow-Small(LSS) targets are problems to be solved due to their good concealment in urban environment. Consequently, the problem of target assignment is studied in this paper. Firstly, a target assignment model based on heterogeneous sensor networks is proposed. The characteristics of LSS targets and the influence of environmental factors such as climate and terrain are considered on this model. At the same time, the detection capabilities of different detection nodes and collaborative tracking capabilities are also considered. Secondly, an intelligent optimization algorithm GSAPSO based on PSO is proposed to maximize tracking effect of the sensors. Finally, the simulations are used to compare the GSAPSO algorithm with the traditional PSO and GAPSO algorithms. The results demonstrate that the proposed algorithm has better tracking effect.
This paper presents an improved deep deterministic policy gradient algorithm based on a six-DOF(six multi-degree-offreedom) arm robot. First, we build a robot model based on the DH(Denavit-Hartenberg) parameters of th...
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This paper presents an improved deep deterministic policy gradient algorithm based on a six-DOF(six multi-degree-offreedom) arm robot. First, we build a robot model based on the DH(Denavit-Hartenberg) parameters of the UR5 arm robot. Then,we improved the experience pool of the traditional DDPG(deep deterministic policy gradient) algorithm by adding a success experience pool and a collision experience pool. Next, the reward function is improved to increase the degree of successful reward and the penalty of collision. Finally, the training is divided into segments, the front three axes are trained first, and then the six axes. The simulation results in ROS(Robot Operating System) show that the improved DDPG algorithm can effectively solve the problem that the six-DOF arm robot moves too far in the configuration space. The trained model can reach the target area in five steps. Compared with the traditional DDPG algorithm, the improved DDPG algorithm has fewer training episodes,but achieves better results.
In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visualinertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagat...
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In this paper, we propose a closed-form IMU error state covariance integration method for optimization-based visualinertial state estimator. We derive a closed-form solutions of the IMU error state covariance propagation in pre-integration process, yielding improved accuracy of IMU residual information matrix. Our visual-inertial state estimator is based on a tightly-coupled, sliding-window optimization framework, which jointly estimate the IMU states and landmarks and performing marginalization to limit the computational cost. Finally, the system are validated in park environment dataset, the result shows our proposed method is effective.
Analytic Hierarchy Process(AHP) is a multi criteria decision-making method,which can describe and transform the qualitative problems quantitatively,and then get the quantitative analysis results in accordance with t...
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Analytic Hierarchy Process(AHP) is a multi criteria decision-making method,which can describe and transform the qualitative problems quantitatively,and then get the quantitative analysis results in accordance with the causal relationship between decision *** this paper,a granular Analytic Hierarchy Process,which introduces the granularity mechanism,is proposed to solve the portfolio selection problem under the mean-risk *** the proposed method,the scale value of scheme layer is no longer limited to nine positive integers from 1 to 9,which gives granularity attributes to the comparison of advantages and disadvantages in a specific criterion layer between different *** proposed method reflects small differences between different alternative schemes through granularity attribute,so it can provide rich decision information for decision *** numeric examples from the real-world financial market(China Shanghai Stock Exchange) are provided to illustrate an essence of the proposed method.
In this paper, we present a trajectory generation method for quadrotor while tracking a moving target with relative pattern. Compared to existing methods, safe flying zone, vehicle’s physical limits and smoothness ar...
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In this paper, we present a trajectory generation method for quadrotor while tracking a moving target with relative pattern. Compared to existing methods, safe flying zone, vehicle’s physical limits and smoothness are considered to ensure obstacles avoidance and real tracking flight. To tackle with the cluttered environment, a parallel Particle Swarm Optimization algorithm is applied to calculate the safe feasible waypoints for quadrotor with consideration of target’s predicted state and the realistic tracking pattern, as well as safety. Then, a multiple waypoints constraints motion planning method is applied and embed into a cost function for solving the problem of trajectories generation and robust tracking for quadrotor via convex optimization approach, ensuring the real-time planning, with the geometrical constraint of safe flight corridor for quadrotor. The effectiveness of proposed method is verified by numerical simulation experiments.
As an interdisciplinary of fuzzy theory and clustering, Fuzzy C-Means(FCM) is widely applied for identifying categories with unlabeled data. However, its application to data which is hard to visualize rises the diff...
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As an interdisciplinary of fuzzy theory and clustering, Fuzzy C-Means(FCM) is widely applied for identifying categories with unlabeled data. However, its application to data which is hard to visualize rises the difficulty for users to determine the input parameters, especially for the number of clusters. In this paper, a kind of fuzzy clustering algorithm with self-regulated parameters named Density-Based Fuzzy C-Means(DBFCM) is proposed by integrating the idea of Density-Based Spatial Clustering of Application with Noise(DBSCAN) into FCM. Its advantage is using the inherit density characteristic of input data to self-determine the parameters of fuzzy clustering. The experimental results demonstrate that the proposed DBFCM can not only self-determine the proper parameters, but also accelerate the convergence process compared to the original FCM.
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