A review of millimeter-wave radar research

With the rapid development of scientifi c research and the maturity of technology, millimeter-wave radar has become the focus of research in industrial production, national defense construction and other fi elds because of its high precision and high applicability. This paper introduces the application fields and algorithm development of millimeter wave radar, expounds the common application scenarios of millimeter wave radar, and gradually elaborates the development and update of radar detection algorithm, on this basis, the new research direction of millimeter wave radar and the improved algorithm idea of FMCW millimeter wave radar detection algorithm are proposed

An Experimental Study on Shear Performance of Adhesive Interface between Steel Plates and CFRP

CFRP (Carbon Fiber Reinforced Polymer) are widely used in steel structural reinforcement. For steel structures strengthened with CFRP, except the cases the structures have defects before strengthening, the adhesive interface is the weakest part and CFRP debonding is the most common failure mode. In order to investigate the failure mechanism of CFRP strengthened steel structures, this paper presents an experimental study on shear performance of adhesive interface between steel plate and CFRP by twin shear model. Six steel plates strengthened with CFRP are divided into two groups, one has no damage, another has a gap at the mid. The specimens are tested under tensile loadings. The study results show that, the plates with a gap failed for CFRPs debonding, the cracking loading and breaking loading are 14.85kN, and 17.88kN respectively; the strain-loading curves had long linear stages, two strains decrease and other strains of another side increased rapidly at the cracking loading, then they both rose until the plates failed

WeCheck: Strong Factual Consistency Checker via Weakly Supervised Learning

A crucial issue of current text generation models is that they often uncontrollably generate factually inconsistent text with respective of their inputs. Limited by the lack of annotated data, existing works in evaluating factual consistency directly transfer the reasoning ability of models trained on other data-rich upstream tasks like question answering (QA) and natural language inference (NLI) without any further adaptation. As a result, they perform poorly on the real generated text and are biased heavily by their single-source upstream tasks. To alleviate this problem, we propose a weakly supervised framework that aggregates multiple resources to train a precise and efficient factual metric, namely WeCheck. WeCheck first utilizes a generative model to accurately label a real generated sample by aggregating its weak labels, which are inferred from multiple resources. Then, we train the target metric model with the weak supervision while taking noises into consideration. Comprehensive experiments on a variety of tasks demonstrate the strong performance of WeCheck, which achieves a 3.4\% absolute improvement over previous state-of-the-art methods on TRUE benchmark on average.Comment: ACL 2023 Main Conferenc