Creating group choreography from music is crucial in cultural entertainment and virtual reality, with a focus on generating harmonious movements. Despite growing interest, recent approaches often struggle with two major challenges: multi-dancer collisions and single-dancer foot sliding. To address these challenges, we propose a Trajectory-Controllable Diffusion (TCDiff) framework, which leverages non-overlapping trajectories to ensure coherent and aesthetically pleasing dance movements. To mitigate collisions, we introduce a Dance-Trajectory Navigator that generates collision-free trajectories for multiple dancers, utilizing a distance-consistency loss to maintain optimal spacing. Furthermore, to reduce foot sliding, we present a footwork adaptor that adjusts trajectory displacement between frames, supported by a relative forward-kinematic loss to further reinforce the correlation between movements and trajectories. Experiments demonstrate our method's superiority.

In this paper, we introduce DCDepth, a novel framework for the long-standing monocular depth estimation task. Moving beyond conventional pixel-wise depth estimation in the spatial domain, our approach estimates the frequency coefficients of depth patches after transforming them into the discrete cosine domain. This unique formulation allows for the modeling of local depth correlations within each patch. Crucially, the frequency transformation segregates the depth information into various frequency components, with low-frequency components encapsulating the core scene structure and high-frequency components detailing the finer aspects. This decomposition forms the basis of our progressive strategy, which begins with the prediction of low-frequency components to establish a global scene context, followed by successive refinement of local details through the prediction of higher-frequency components. We conduct comprehensive experiments on NYU-Depth-V2, TOFDC, and KITTI datasets, and demonstrate the state-of-the-art performance of DCDepth. Code is available at https://github.com/w2kun/DCDepth.

Aiming at the characteristics of unstable wind power during the ship's sailing process, this paper uses a multi-lithium battery-supercapacitor hybrid energy storage system to store electrical energy, and stabilizes the severe fluctuations in wind power. The utilization of electrical energy in the ship's power grid has been improved. Utilizing the fast charging and discharging characteristics of supercapacitors, a control strategy for supercapacitor switching charging and discharging was designed to improve the energy utilization rate.

In the recent years, the automatous operation of All Electric Ships (AES) has gained a lot of attention from both academia and industrial researchers as one of the most promising prospects for the transformational control development of new generation electric ships. In this paper, the reconfiguration problem of shipboard power system (SPS) considering external threats is investigated from the perspective of hybrid and distributed. By constructing a two-layer distributed control framework, mission commands are interpreted by coordinator and then send to zonal controllers, while reconfiguration actions can be performed in-zone by zonal controllers or among multi-zones by coordinator to complete the mission. Based on the extended hybrid model of SPS developed, the subsystem reconfiguration and coordinate algorithm are proposed. Finally the practical example of SPS facing threats is provided to illustrate the process to choose the optimal reconfiguration strategy.