this paper investigates a distributed prescribed-time observer design for a type of multi-agent systems under homologous sensor attacks. First, we design Luenberger-like observers based on attack consensus which can a...
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ISBN:
(纸本)9798331518509;9798331518493
this paper investigates a distributed prescribed-time observer design for a type of multi-agent systems under homologous sensor attacks. First, we design Luenberger-like observers based on attack consensus which can accurately obtain the state of the system at prescribed time. then, the sufficiency condition and construction algorithm of the observer gain matrix design are given. Compared withthe existing results of distributed state estimation under homologous sensor attacks, the observer proposed in this paper does not need to use all data in the time window and the prescribed convergence time is independent of the initial value of the system. Simulation results validate the usefulness of the designed observer.
this paper proposes an improved semantic segmentation model based on Fully Convolutional Network(FCN). Firstly, this paper integrates the channel attention module into the backbone of FCN to identify the channels that...
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ISBN:
(纸本)9798331518509;9798331518493
this paper proposes an improved semantic segmentation model based on Fully Convolutional Network(FCN). Firstly, this paper integrates the channel attention module into the backbone of FCN to identify the channels that are more important for semantic segmentation. Secondly, this paper adds a multi-scale feature fusion module to the network to integrate multi-scale feature information. thirdly, this paper replaces some of standard convolutions in the backbone network with dilated convolutions to increase the receptive field. Experimental results demonstrate that after adding the two modules, the pixel accuracy can reach 75.8%, and the mIOU value has increased by 2.8% compared to the original FCN model. Moreover, utilizing pre-trained weights during the training process can greatly enhance the performance of the model.
Multipath communication has become a viable alternative to costly quality-of-service networking solutions. Despite multiple advantages, multipath transport protocols suffer from aggravated protocol delay and jitter or...
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ISBN:
(纸本)9798331518509;9798331518493
Multipath communication has become a viable alternative to costly quality-of-service networking solutions. Despite multiple advantages, multipath transport protocols suffer from aggravated protocol delay and jitter originating from path heterogeneity. the algorithms responsible for splitting the data over the channels developed so far rely on the evaluation of the most recent channel properties. Taking into account the time lag between the decision on the path allocation and experiencing its effects, the standard approaches are vulnerable to the temporal fluctuations of transmission delays. the paper shows how to use the past and current information about the receiver state to form a zero-sum game among the paths. Contrary to the typical scheduler logic, the path is rewarded and penalized by the receiver irrespective of the channel-related readings. As a result, a prompt reaction to the changes in networking conditions is obtained, and the surges of protocol delay owing to Head-of-Line blocking are mitigated.
In this paper, we investigate the use of multi-UAVs (Unmanned Aerial Vehicles) for defense penetration in an urban environment with obstacles. To ensure a high-quality scenario of the urban environment, a digital-twin...
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ISBN:
(纸本)9798331518509;9798331518493
In this paper, we investigate the use of multi-UAVs (Unmanned Aerial Vehicles) for defense penetration in an urban environment with obstacles. To ensure a high-quality scenario of the urban environment, a digital-twin-city simulation platform is proposed featuring a powerful physics engine and real-time interaction technology. Subsequently, an improved multi-agent soft actor-critic (MASAC) method with a target prediction network is introduced to enhance the effectiveness of the cooperative multi-UAV system in defense penetration missions. Finally, the performance of the proposed improved MASAC method is evaluated through comparative experiments.
this paper studies the detection and identification problem of malicious network traffic and proposes a rule extraction algorithm based on interpretable models. the algorithm utilizes interpretable greedy trees as the...
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ISBN:
(纸本)9798331518509;9798331518493
this paper studies the detection and identification problem of malicious network traffic and proposes a rule extraction algorithm based on interpretable models. the algorithm utilizes interpretable greedy trees as the foundational model and extends its applicability to handle multi-classification problems, thereby enhancing interpretability and detection accuracy. this methodology furnishes a more dependable and secure framework for discerning attack categories within the domain of network security. the experiment results show that the proposed algorithm achieves better balance between interpretability and identification precision.
In autonomous robotics, a significant challenge involves devising robust solutions for Active Collaborative SLAM (AC-SLAM). this process requires multiple robots to cooperatively explore and map an unknown environment...
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ISBN:
(纸本)9798331518509;9798331518493
In autonomous robotics, a significant challenge involves devising robust solutions for Active Collaborative SLAM (AC-SLAM). this process requires multiple robots to cooperatively explore and map an unknown environment by intelligently coordinating their movements and sensor data acquisition. In this article, we present an efficient visual AC-SLAM method using aerial and ground robots for environment exploration and mapping. We propose an efficient frontiers filtering method that takes into account the common IoU map frontiers and reduces the frontiers for each robot. Additionally, we present an approach to guide robots to previously visited goal positions to promote loop closure and reduce SLAM uncertainty. the proposed method is implemented in ROS and evaluated through simulations on publicly available datasets and similar methods, achieving an accumulative average of 59% increase in area coverage.
the tracking performance and line-of-sight stabilization of electro-optical stabilized servo tracking systems are often affected by various disturbances and uncertainties. To address the problems, a robust motion cont...
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ISBN:
(纸本)9798331518509;9798331518493
the tracking performance and line-of-sight stabilization of electro-optical stabilized servo tracking systems are often affected by various disturbances and uncertainties. To address the problems, a robust motion control scheme is developed. the control strategy effectively amalgamates sliding mode control (SMC), extended state observer (ESO), and adaptive radial basis function (RBF) neural network. Specifically, an ESO is constructed to estimate unknown disturbances and uncertainties in the system. An RBF neural network is employed to correct the estimation accuracy of ESO for time-varying disturbances. Furthermore, the sliding model control strategy is combined with ESO and RBF neural network to mitigate the impact of multi-source disturbances and uncertainties on control precision. the asymptotic stability of the system is demonstrated through Lyapunov theory and LaSalle invariance principle. Finally, simulations are implemented to illustrate the superiority and effectiveness of the proposed control method compared to the traditional SMC-ESO method.
this paper deals withthe problem of robust trajectory tracking of a robot interacting with a human and subject to uncertainties and the problem of chattering in sliding mode. Indeed, a new controller for robotic mani...
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ISBN:
(纸本)9798331518509;9798331518493
this paper deals withthe problem of robust trajectory tracking of a robot interacting with a human and subject to uncertainties and the problem of chattering in sliding mode. Indeed, a new controller for robotic manipulator systems using terminal model-based sliding manifolds is proposed. Moreover, a bi-power reaching law with variable-gains is designed to reduce the phenomenon of chattering and to ensure fixed-time stability of the exoskeleton robot trajectories into the sliding manifolds. the chattering is not reduced thanks to the new reaching law only but also thanks to the designed model-based sliding manifolds that allow a decoupled control inputs. the proposed controller is experimentally implemented on an upper-limb rehabilitation exoskeleton robot with seven rotary joints. A Comparison study with super-twisting second-order sliding mode is also presented to show the effectiveness of the developed technique.
In this paper, the data-driven optimal control problem is studied for continuous-time linear nonzero-sum games. Two kinds of reinforcement learning algorithms, i.e., reinforcement learning algorithm with data-storage ...
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ISBN:
(纸本)9798331518509;9798331518493
In this paper, the data-driven optimal control problem is studied for continuous-time linear nonzero-sum games. Two kinds of reinforcement learning algorithms, i.e., reinforcement learning algorithm with data-storage based least-square method and reinforcement learning algorithm with filter based least-square method, are presented to obtain the Nash equilibrium solution. the properties of the presented reinforcement learning algorithms are analyzed. Simulation results show the efficiency of the presented reinforcement learning algorithms.
GPS is widely used for the localization of unmanned aerial vehicles due to its universal nature and high precision when combined with an IMU. However, a GPS relies on communication with satellites, and in some scenari...
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ISBN:
(纸本)9798331518509;9798331518493
GPS is widely used for the localization of unmanned aerial vehicles due to its universal nature and high precision when combined with an IMU. However, a GPS relies on communication with satellites, and in some scenarios, this communication can be blocked rendering the method unusable. In these scenarios, an alternative localization method is needed as relying entirely on an IMU-based approach results in high degrees of drift. A solution to this problem is using a camera, to track the takeoff position of the UAV, and utilizing the pin-hole model to estimate the distance to the takeoff position. the greatest strength of this solution is that the UAV can locate itself without relying on communication. However, there are several challenges associated with utilizing a single camera for localization, especially at long distances, as the noise quickly grows. this paper presents a solution to this problem and a real-life implementation displaying the viability of the method.
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