Deployment of multiple unmanned agent systems to perform missions in a collaborative manner has become a research hotspot in recent years, and the same is true for multiple unmanned surface vehicles (USVs). Therefore,...
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Deployment of multiple unmanned agent systems to perform missions in a collaborative manner has become a research hotspot in recent years, and the same is true for multiple unmanned surface vehicles (USVs). Therefore, this present paper mainly focuses on the coordination control for multiple USVs in the dynamic maritime environment. Approach used in this paper is principally the behavior-based method, which is useful to guide the unmanned agent system in an unknown or dynamically changing environment. The hybrid behavior-based (HB) method, combining the null-space-basedbehavioral (NSB) approach and behaviors complied with the International Regulations for Preventing Collisions at Sea (COLREGs), is proposed to achieve the coordination control of multiple USVs. Using the HB method, all aspects of the coordination control problem, including the rendezvous issue, formation keeping and collision avoidance of both static and dynamic obstacles, can be accomplished at the same time. Furthermore, the Kalman filter (KF) algorithm is incorporated into the proposed HB method to compensation for navigation information of other traffic ships in the dynamic environment. So that a more practical coordination control for multiple USVs in the realistic applications can be achieved. Through the verification by numerical simulations, the proposed HB method can effectively achieve the coordination control for multiple USVs in the dynamic environment.
Low-rate attacks can conceal their traffic because their packets are at very low rates, which make it easy to bury themselves into the normal traffic. Thus, although a number of volume-based detection techniques are a...
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ISBN:
(纸本)9780769547374;9781467320016
Low-rate attacks can conceal their traffic because their packets are at very low rates, which make it easy to bury themselves into the normal traffic. Thus, although a number of volume-based detection techniques are able to identify anomalies that trigger significant changes in traffic volume, they are not applicable to detecting low-rate attacks. Because of this, the problem of low-rate attacks has been attracting many researchers in the community of network security. In this study, for the first time we propose a methodbased on the normal behavior mode of traffic to detect outbreaks of low-rate attacks. The experimental result indicates that our proposal is efficient.
Unmanned Aerial Vehicle (UAV) swarm collaboration enhances mission effectiveness. However, fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments. Aim...
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Unmanned Aerial Vehicle (UAV) swarm collaboration enhances mission effectiveness. However, fixed-wing UAV swarm flights face collaborative safety control problems within a limited airspace in complex environments. Aimed at the cooperative control problem of fixed-wing UAV swarm flights under the airspace constraints of a virtual tube in a complex environment, this paper proposes a behavior-based distributed control method for fixed-wing UAV swarm considering flight safety constraints. Considering the fixed-wing UAV swarm flight problem in complex environment, a virtual tube model based on generator curve is established. The tube keeping, centerline tracking and flight safety behavioral control strategies of the UAV swarm are designed to ensure that the UAV swarm flies along the inside of the virtual tube safety and does not go beyond its boundary. On this basis, a maneuvering decision-making methodbased on behavioral fusion is proposed to ensure the safe flight of UAV swarm in the restricted airspace. This cooperative control method eliminates the need for respective pre-planned trajectories, reduces communication requirements, and achieves a high level of intelligence. Simulation results show that the proposed behavior-based UAV swarm cooperative control method is able to make the fixed-wing UAV swarm, which is faster and unable to hover, fly along the virtual tube airspace under various virtual tube shapes and different swarm sizes, and the spacing between the UAVs is larger than the minimum safe distance during the flight.
We aim to improve the efficiency of our previously proposed anti-malware hardware;it is a hardware-implemented malware detection mechanism that uses information inside the processor. We previously evaluated a prototyp...
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We aim to improve the efficiency of our previously proposed anti-malware hardware;it is a hardware-implemented malware detection mechanism that uses information inside the processor. We previously evaluated a prototype, but, due to its prototypical nature, there remain limitations, such as only detecting certain behaviors, high power consumption, and a tendency to bloat the training model. In this paper, we propose a circuit and a learning method to achieve high efficiency, low power consumption, and light weight for the model. In considering these three issues, we focus on time-series metadata obtained by transforming the processor information. To improve efficiency, we implement predictive detection to predict the behavior of metadata in the malware detection component. This lets the model detect malware within less than 19% of the number of execution cycles of the conventional method. To reduce power consumption, we implement a sampling circuit that interrupts the input to the detection circuit at regular intervals, reducing the system's uptime by 99% while maintaining judgment accuracy. Finally, for a light weight, we focus on the training process of the metadata generator based on a machine-learning model. By applying sampling learning and feature dimensionality reduction in the training process, a metadata generator approximately 16% smaller than the previous version is created.
This paper proposes a decentralized behavior-based formation control algorithm for multiple robots considering obstacle avoidance. Using only the information of the relative position of a robot between neighboring rob...
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This paper proposes a decentralized behavior-based formation control algorithm for multiple robots considering obstacle avoidance. Using only the information of the relative position of a robot between neighboring robots and obstacles, the proposed algorithm achieves formation control based on a behavior-based algorithm. In addition, the robust formation is achieved by maintaining the distance and angle of each robot toward the leader robot without using information of the leader robot. To avoid the collisions with obstacles, the heading angles of all robots are determined by introducing the concept of an escape angle, which is related with three boundary layers between an obstacle and the robot. The layer on which the robot is located determines the start time of avoidance and escape angle;this, in turn, generates the escape path along which a robot can move toward the safe layer. In this way, the proposed method can significantly simplify the step of the information process. Finally, simulation results are provided to demonstrate the efficiency of the proposed algorithm.
In this paper, a behavior-based navigation control approach with an improved artificial potential field (APF) method is proposed for swarm robots. In this approach a multi-group coordination control strategy is adopte...
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ISBN:
(纸本)9781479947249
In this paper, a behavior-based navigation control approach with an improved artificial potential field (APF) method is proposed for swarm robots. In this approach a multi-group coordination control strategy is adopted to solve the coordination problems for distributed control and several behaviors are designed for the navigation control of swarm robots to overcome the local minimum and oscillation problems. An improved APF method is applied for the implementation of these behaviors. Numerical results are given to demonstrate the success of the proposed method.
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