Abstract: An inductor can include at least one winding, where each winding comprises a coil body and at least two lead-out terminals being in contact with the coil body; a first encapsulation body configured to at least encapsulate part of the lead-out terminals and part of the coil body, and to expose the lead-out terminals; and where the first encapsulation body includes an insulating main material and magnetic particles dispersed in the insulating main material.

Abstract: A module structure can include a first type structure including a first encapsulation body having a magnetic property, and at least one inductive element, where at least part of the inductive element is encapsulated in the first encapsulation body; a second type structure including a second encapsulation body having a non-magnetic property, and at least one non-inductive element, where the non-inductive element is encapsulated in the second encapsulation body; and pin structures located on exposed surfaces of the first type structure and/or the second type structure, in order to lead out corresponding electrodes.

Abstract: The present disclosure provides a method for multimodal emotion classification based on modal space assimilation and contrastive learning. The present disclosure introduces the concept of assimilation. A guidance vector composed of complementary information between modalities is utilized to guide each modality to simultaneously approach a solution space. This operation not only further improves the efficiency of searching for the solution space but also renders heterogeneous spaces of three modalities isomorphic. In a process of making spaces isomorphic, contributions of a plurality of modalities to a final solution space can be effectively balanced to a certain extent. When guiding each modality, this strategy enables a model to be more concerned about emotion features, thereby reducing intra-modal redundancy. Thus, the difficulty of establishing a multimodal representation is reduced.

Abstract: A three-dimensional model-based coverage path planning method for an unmanned aerial vehicle, including determining a size of a view frustum of the unmanned aerial vehicle, and establishing a bounding box of the three-dimensional model; designing a three-dimensional grid map according to the view frustum and the bounding box, and defining an attribute of a grid, and arranging a measurement point of the unmanned aerial vehicle on a surface of the three-dimensional model; and planning an unmanned aerial vehicle measurement path on the surface of the three-dimensional model according to the arrangement of measurement points.

Abstract: A three-dimensional model-based coverage path planning method for an unmanned aerial vehicle, including determining a size of a view frustum of the unmanned aerial vehicle, and establishing a bounding box of the three-dimensional model; designing a three-dimensional grid map according to the view frustum and the bounding box, and defining an attribute of a grid, and arranging a measurement point of the unmanned aerial vehicle on a surface of the three-dimensional model; and planning an unmanned aerial vehicle measurement path on the surface of the three-dimensional model according to the arrangement of measurement points.