The LLC converter, known for its soft-switching properties, is widely used in industry. To balance the best steady-state and dynamic performance and to facilitate controller design, the small-signal model is critical. Recently, based on the time domain trajectory under perturbation and the extended description function, Y. -H. Hsieh proposed an accurate modeling method of bang-bang charge-controlled LLC converter. However, it did not consider the impact on the model of adding ramp compensation, which is commonly introduced in industrial applications to improve the stability of charge control. In addition, the modeling required consistent control logic between positive and negative half-cycles, i.e., the symmetric charge control. When dealing with asymmetric charge control, such as current-integrating charge control, it becomes inaccurate. This paper explores the extension of this modeling approach to LLC converters adopting asymmetric charge control with the consideration of ramp compensation. The small signal model is subsequently established, and both simulation and experimental results validate its accuracy.

The pre-trained text-image discriminative models, such as CLIP, has been explored for open-vocabulary semantic segmentation with unsatisfactory results due to the loss of crucial localization information and awareness of object shapes. Recently, there has been a growing interest in expanding the application of generative models from generation tasks to semantic segmentation. These approaches utilize generative models either for generating annotated data or extracting features to facilitate semantic segmentation. This typically involves generating a considerable amount of synthetic data or requiring additional mask annotations. To this end, we uncover the potential of generative text-to-image diffusion models (e.g., Stable Diffusion) as highly efficient open-vocabulary semantic segmenters, and introduce a novel training-free approach named DiffSegmenter. The insight is that to generate realistic objects that are semantically faithful to the input text, both the complete object shapes and the corresponding semantics are implicitly learned by diffusion models. We discover that the object shapes are characterized by the self-attention maps while the semantics are indicated through the cross-attention maps produced by the denoising U-Net, forming the basis of our segmentation results. Additionally, we carefully design effective textual prompts and a category filtering mechanism to further enhance the segmentation results. Extensive experiments on three benchmark datasets show that the proposed DiffSegmenter achieves impressive results for open-vocabulary semantic segmentation.

3D point cloud semantic segmentation has a wide range of applications. Recently, weakly supervised point cloud segmentation methods have been proposed, aiming to alleviate the expensive and laborious manual annotation process by leveraging scene-level labels. However, these methods have not effectively exploited the rich geometric information (such as shape and scale) and appearance information (such as color and texture) present in RGB-D scans. Furthermore, current approaches fail to fully leverage the point affinity that can be inferred from the feature extraction network, which is crucial for learning from weak scene-level labels. Additionally, previous work overlooks the detrimental effects of the long-tailed distribution of point cloud data in weakly supervised 3D semantic segmentation. To this end, this paper proposes a simple yet effective scene-level weakly supervised point cloud segmentation method with a newly introduced multi-modality point affinity inference module. The point affinity proposed in this paper is characterized by features from multiple modalities (e.g., point cloud and RGB), and is further refined by normalizing the classifier weights to alleviate the detrimental effects of long-tailed distribution without the need of the prior of category distribution. Extensive experiments on the ScanNet and S3DIS benchmarks verify the effectiveness of our proposed method, which outperforms the state-of-the-art by ~4% to ~ 6% mIoU. Codes are released at https://github.com/Sunny599/AAAI24-3DWSSG-MMA.

The conjunctive model of skill map reflects a way to solve the items in a knowledge domain. Currently, a skill map has been transformed into a formal context to construct a knowledge structure. In fact, the conjunctive model of skill map can be regarded as a special case of skill function. In this paper, we consider a way to solve the items in a knowledge domain as a scale. As a result, a skill function can be decomposed into multiple formal contexts, which correspond to a multi-scale context. Based on this, a method for constructing a knowledge structure delineated via competency model by skill function is proposed. In addition, a method for finding minimal skill function is proposed to reduce the complexity. Last but not least, the experimental analysis of five data sets on the UCI Repository shows that the improved method of constructing knowledge structure is effective and feasible and that it is necessary to perform skill reduction on skill function.

With the development of renewable energy and the widespread use of electric vehicles, the AC/DC converter, which plays an important role as an interface to the power grid, has drawn much attention. Compared to the two stage AC/DC converter, the single stage AC/DC converter has the merits of high power density and high efficiency. with introducing the LLC resonant tank, the converter can achieve soft switching operation and wide voltage range. However, in the LLC-type single stage AC/DC converter, the control method is usually the Pulse Frequency Modulation (PFM), which has less outstanding dynamic response faced with sudden changes in load. To improve the dynamic response, this paper proposes a charge control strategy for the single stage AC/DC converter. By introducing charge control as the inner loop, the bandwidth can be extended. The simulation of an LLC-type matrix AC/DC converter is implemented to verify the proposed control strategy. A 1kW prototype is built and the preliminary experiment is conducted. The simulation and the preliminary experimental results are in good agreement with the theoretical analysis.