A Comparison Study of Two Methods for Elliptic Boundary Value Problems

In this paper, we perform a comparison study of two methods (the embedded boundary method and several versions of the mixed finite element method) to solve an elliptic boundary value problem

Multi-objective Optimization Based on Improved Differential Evolution Algorithm

On the basis of the fundamental differential evolution (DE), this paper puts forward several improved DE algorithms to find a balance between global and local search and get optimal solutions through rapid convergence. Meanwhile, a random mutation mechanism is adopted to process individuals that show stagnation behaviour. After that, a series of frequently-used benchmark test functions are used to test the performance of the fundamental and improved DE algorithms. After a comparative analysis of several algorithms, the paper realizes its desired effects by applying them to the calculation of single and multiple objective functions

KERM: Knowledge Enhanced Reasoning for Vision-and-Language Navigation

Vision-and-language navigation (VLN) is the task to enable an embodied agent to navigate to a remote location following the natural language instruction in real scenes. Most of the previous approaches utilize the entire features or object-centric features to represent navigable candidates. However, these representations are not efficient enough for an agent to perform actions to arrive the target location. As knowledge provides crucial information which is complementary to visible content, in this paper, we propose a Knowledge Enhanced Reasoning Model (KERM) to leverage knowledge to improve agent navigation ability. Specifically, we first retrieve facts (i.e., knowledge described by language descriptions) for the navigation views based on local regions from the constructed knowledge base. The retrieved facts range from properties of a single object (e.g., color, shape) to relationships between objects (e.g., action, spatial position), providing crucial information for VLN. We further present the KERM which contains the purification, fact-aware interaction, and instruction-guided aggregation modules to integrate visual, history, instruction, and fact features. The proposed KERM can automatically select and gather crucial and relevant cues, obtaining more accurate action prediction. Experimental results on the REVERIE, R2R, and SOON datasets demonstrate the effectiveness of the proposed method.Comment: Accepted by CVPR 2023. The code is available at https://github.com/XiangyangLi20/KER

Energy-efficient Integrated Sensing and Communication System and DNLFM Waveform

Integrated sensing and communication (ISAC) is a key enabler of 6G. Unlike communication radio links, the sensing signal requires to experience round trips from many scatters. Therefore, sensing is more power-sensitive and faces a severer multi-target interference. In this paper, the ISAC system employs dedicated sensing signals, which can be reused as the communication reference signal. This paper proposes to add time-frequency matched windows at both the transmitting and receiving sides, which avoids mismatch loss and increases energy efficiency. Discrete non-linear frequency modulation (DNLFM) is further proposed to achieve both time-domain constant modulus and frequency-domain arbitrary windowing weights. DNLFM uses very few Newton iterations and a simple geometrically-equivalent method to generate, which greatly reduces the complex numerical integral in the conventional method. Moreover, the spatial-domain matched window is proposed to achieve low sidelobes. The simulation results show that the proposed methods gain a higher energy efficiency than conventional methods