Non-singular Cooperative Guiding Vector Field Under a Homotopy Equivalence Transformation

The present article advances the concept of a non-singular cooperative guiding vector field under a homotopy equivalence transformation. Firstly, the derivation of a guiding vector field, based on a non-singular vector field, is elaborated to navigate a transformed path from another frame. The existence of such vector fields is also deliberated herein. Subsequently, a coordination vector field derived from the guiding vector field is presented, incorporating an in-depth analysis concerning the impact of the vector field parameters. Lastly, the practical implementation of this novel vector field is demonstrated by its applications to 2-D and 3-D cooperative moving path following issues, establishing its efficacy.Comment: 12 pages, 12 figures, submitting to TAC at presen

A Frequency-Domain Path-Following Method for Discrete Data-Based Paths

This paper presents a novel frequency-domain approach for path following problem, specifically designed to handle paths described by discrete data. The proposed algorithm utilizes the fast Fourier Transform (FFT) to process the discrete path data, enabling the construction of a non-singular guiding vector field. This vector field serves as a reference direction for the controlled robot, offering the ability to adapt to different levels of precision. Additionally, the frequency-domain nature of the vector field allows for the reduction of computational complexity and effective noise suppression. The efficacy of the proposed approach is demonstrated through a numerical simulation, and theoretical analysis provides an upper bound for the ultimate mean-square path-following error

Direct-PoseNet: Absolute Pose Regression with Photometric Consistency

We present a relocalization pipeline, which combines an absolute pose regression (APR) network with a novel view synthesis based direct matching module, offering superior accuracy while maintaining low inference time. Our contribution is twofold: i) we design a direct matching module that supplies a photometric supervision signal to refine the pose regression network via differentiable rendering; ii) we modify the rotation representation from the classical quaternion to SO(3) in pose regression, removing the need for balancing rotation and translation loss terms. As a result, our network Direct-PoseNet achieves state-of-the-art performance among all other single-image APR methods on the 7-Scenes benchmark and the LLFF dataset

Big AI Models for 6G Wireless Networks: Opportunities, Challenges, and Research Directions

Recently, big artificial intelligence models (BAIMs) represented by chatGPT have brought an incredible revolution. With the pre-trained BAIMs in certain fields, numerous downstream tasks can be accomplished with only few-shot or even zero-shot learning and exhibit state-of-the-art performances. As widely envisioned, the big AI models are to rapidly penetrate into major intelligent services and applications, and are able to run at low unit cost and high flexibility. In 6G wireless networks, to fully enable intelligent communication, sensing and computing, apart from providing other intelligent wireless services and applications, it is of vital importance to design and deploy certain wireless BAIMs (wBAIMs). However, there still lacks investigation on architecture design and system evaluation for wBAIM. In this paper, we provide a comprehensive discussion as well as some in-depth prospects on the demand, design and deployment aspects of the wBAIM. We opine that wBAIM will be a recipe of the 6G wireless networks to build high-efficient, sustainable, versatile, and extensible wireless intelligence for numerous promising visions. Then, we provide the core characteristics, principles, and pilot studies to guide the design of wBAIMs, and discuss the key aspects of developing wBAIMs through identifying the differences between the existing BAIMs and the emerging wBAIMs. Finally, related research directions and potential solutions are outlined

ConvD: Attention Enhanced Dynamic Convolutional Embeddings for Knowledge Graph Completion

Knowledge graphs generally suffer from incompleteness, which can be alleviated by completing the missing information. Deep knowledge convolutional embedding models based on neural networks are currently popular methods for knowledge graph completion. However, most existing methods use external convolution kernels and traditional plain convolution processes, which limits the feature interaction capability of the model. In this paper, we propose a novel dynamic convolutional embedding model ConvD for knowledge graph completion, which directly reshapes the relation embeddings into multiple internal convolution kernels to improve the external convolution kernels of the traditional convolutional embedding model. The internal convolution kernels can effectively augment the feature interaction between the relation embeddings and entity embeddings, thus enhancing the model embedding performance. Moreover, we design a priori knowledge-optimized attention mechanism, which can assign different contribution weight coefficients to multiple relation convolution kernels for dynamic convolution to improve the expressiveness of the model further. Extensive experiments on various datasets show that our proposed model consistently outperforms the state-of-the-art baseline methods, with average improvements ranging from 11.30\% to 16.92\% across all model evaluation metrics. Ablation experiments verify the effectiveness of each component module of the ConvD model