Audio-visual target speaker extraction (AV-TSE) aims to extract the specific person’s speech from the audio mixture given auxiliary visual cues. Previous methods usually search for the target voice through speech-lip...
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Integrated sensing and communication (ISAC) in terahertz (THz) networks for delay-sensitive services is regarded as a key enabler for future 6G networks, with ISAC helping to aid beam alignment and improve communicati...
Integrated sensing and communication (ISAC) in terahertz (THz) networks for delay-sensitive services is regarded as a key enabler for future 6G networks, with ISAC helping to aid beam alignment and improve communication reliability in THz networks. Nevertheless, in ISAC systems, there exists resource and performance tradeoffs amongst sensing, communication and computation. For instance, while better sensing accuracy adds information to aid communication beam alignment, it reduces the temporal resources available for communication. Therefore, in this paper we present a joint sensing, communication and computation model, with a non-uniform frame structure allowing the sensing and communication time to be flexibly adjusted. Based on our model, we formulate an average communication outage probability minimization problem, which optimizes the time and carrier frequency allocation schemes, given constrained resources. We then derive in closed-form the available communication outage probability subject to a given delay threshold, decoupling the computation sub-problem from the original problem. Based on this result, we reformulate the original problem into a joint resource allocation problem between sensing and communication, and solve it by applying the Hungarian algorithm. Numerical results show that our strategy outperforms existing baselines in the average communication outage probability performance.
As the number of and capabilities of distributed and inverter-based energy resources increase, there is also an increasing opportunity to utilize such devices to provide resilience to large-scale power outages. During...
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As the number of and capabilities of distributed and inverter-based energy resources increase, there is also an increasing opportunity to utilize such devices to provide resilience to large-scale power outages. During traditional power system restoration, natural hazard disruption or cyber attack of a central controller could cause the necessary processes to halt, but self-determining, decentralized schemes provide new opportunities for robustness. Therefore, a collaborative, autonomous restoration method is proposed and demonstrated in the field on a test microgrid with distributed energy resources. The methodology and test set-up is described and field demonstration results provided. The novel method is shown to effectively black start the microgrid using grid-forming inverter-based resources in a decentralized fashion, enabling future grid reliability even in the face of natural hazards and cyber disruptions.
The Cybertwin-based cloud native network (CCNN) with a cloud-edge-end architecture is to provide personalized user services, which includes both wireless access networks and cloud networks. However, the limited availa...
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Maintaining system frequency at a rated value is a fundamental requirement to ensure the stability and security of power grids. However, the advanced frequency regulation process is more cyber-dependent, and raises a ...
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Maintaining system frequency at a rated value is a fundamental requirement to ensure the stability and security of power grids. However, the advanced frequency regulation process is more cyber-dependent, and raises a cyber-security issue for the power system. Traditional methods cannot avoid the adverse impacts of cyber attacks, which may lead to frequency deviations and, in severe cases, even blackouts. To address this issue, we propose an attack-resilient control algorithm for the smart grid's frequency regulation to defend the system frequency against cyber attacks. The stability of the system with the proposed controller can be guaranteed, which is strictly proved by Lyapunov theorem. Furthermore, the effectiveness of the proposed resilient controller is validated by case studies. The results show that in comparison with the existing methods, the proposed controller can make the system frequency achieve faster recovery with a minor frequency deviation. Specifically, using the proposed method, the maximum frequency deviation can be reduced from 0.17 Hz to approximately 0 Hz even under a cyber attack, which means that the adverse effects of a cyber attack can be almost eliminated. Therefore, the proposed controller can well counter potential cyber attacks, which helps improve the stability of the system frequency.
Model predictive control (MPC) has recently been considered for many permanent magnet synchronous machine (PMSM) drive applications. However, the conventional MPC lacks accurate current tracking and fast dynamic respo...
Model predictive control (MPC) has recently been considered for many permanent magnet synchronous machine (PMSM) drive applications. However, the conventional MPC lacks accurate current tracking and fast dynamic responses in the overmodulation region and the six-step operation, which limits its application areas. This paper proposes a six-step operation scheme based on MPC for PMSM drives in traction applications. The current trajectory long-horizon prediction and the average current-based objective function are introduced to determine the optimal switching instants. To enhance the method’s precision, a trapezoidal integration rule has been used in the prediction step. The voltage angle control principle is introduced to clamp the voltage vectors. The effectiveness of the proposed method has been validated by detailed studies. The proposed method is demonstrated to have excellent current tracking accuracy and fast dynamic response in six-step operation, which is an advantage over existing methods.
Data association plays an important role in forming target tracks when false alarms exist. Its accuracy is key to reducing the computational burden of the combinatorial explosion problem inherent to target tracking in...
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Traffic flow prediction plays a crucial role in the management and operation of urban transportation systems. While extensive research has been conducted on predictions for individual transportation modes, there is re...
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In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been...
In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been increasingly used for 2D to 3D pose estimation tasks since the skeleton topologies can be viewed as graph structures. In this paper, we propose a novel graph convolutional network architecture, Multi-scale Multi-branch Fusion Graph Convolutional Networks (MSMB-GCN), for 3D Human Pose Estimation(3D HPE) task. The proposed model consists of multiple GCN blocks with a multi-branch architecture. This multi-branch architecture enables the model to get multi-scale features for human skeletal representations. The group of GCN blocks, which has strong multi-level feature extraction capabilities, allows the model to learn global and local features, lower-level and higher-level features. Experiment results on the HumanPose benchmark demonstrate that our model outperforms the state-of-the-art and ablation studies validate the effectiveness of our approach.
Audio-visual active speaker detection (AV-ASD) aims to identify which visible face is speaking in a scene with one or more persons. Most existing AV-ASD methods prioritize capturing speech-lip correspondence. However,...
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