We introduce a 3D instance representation, termed instance kernels, where instances are represented by one-dimensional vectors that encode the semantic, positional, and shape information of 3D instances. We show that ...
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Vehicle scheduling plays a profound role in public transit planning. Vehicle scheduling problem (VSP) addresses the task of assigning vehicles to cover timetabled trips to minimize the fleet-size as well as operating ...
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Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability ma...
Gait planning of quadruped robots plays an important role in achieving less walking, including dynamic and static gait. In this article, a static and dynamic gait control method based on center of gravity stability margin is proposed. Firstly, the robot model and kinematics modeling are introduced. Secondly, the robot’s foot static and dynamic gait were planned and the foot trajectory was designed. Finally, two types of gait of the robot were simulated using Vrep simulation software, and the differences in stability and speed between the coordinated gait with speed and stability in the static and dynamic gait of a 12 degree of freedom robot were analyzed, verifying the effectiveness of the gait control method proposed in this paper.
In this paper, we explore the relationship between the injected attack signal and the attack selection strategy in networked control systems where the adversary desires to steer the system state to the expected malici...
In this paper, we explore the relationship between the injected attack signal and the attack selection strategy in networked control systems where the adversary desires to steer the system state to the expected malicious one. We construct a sequential attack framework, i.e., the injected false data varies with the sampling time in discrete-time systems, and then derive an optimal sequential FDI attack strategy. The optimal sequential FDI attack strategy reveals the strongly coupled relationship between the injected attack signal and the attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack strategy and saves computing sources. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack and the relationship between the attack signal and the attack selection strategy.
Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and fun...
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Addiction is a chronic and often relapsing brain disorder characterized by drug abuse and withdrawal symptoms and compulsive drug seeking(Koob and Volkow,2010)when access to the drug is *** leads to structural and functional brain changes implicated in reward,memory,motivation,and control(Volkow et al.,2019;Lüscher et al.,2020).
The thesis studies the semi-global scaled edge-consensus of linear discrete-time multi-agent systems under both the directed networks and undirected networks, where the states of each edge are subject to input saturat...
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Accurately predicting the performance degradation trend of fuel cells helps take measures in advance and prolong the service life of the stack, which led to the creation of a novel hybrid forecasting approach. The fir...
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In this work, we consider the consensus problem of the multi-agent system over matrix-weighted networks using an event-triggered mechanism. An event-triggered coordination strategy is proposed to steer this generalize...
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Motor imagery (MI) is a classical paradigm in electroencephalogram (EEG) based brain-computer interfaces (BCIs). Online accurate and fast decoding is very important to its successful applications. This paper proposes ...
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Entity representation learning is both fundamental and crucial for link prediction in knowledge graph. Existing entity representation learning approaches mainly focus on learning from knowledge triples with single mod...
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