Author name cluster

Wenfeng Li

Possible papers associated with this exact author name in Arrow. This page groups case-insensitive exact name matches and is not a full identity disambiguation profile.

3 papers

1 author row

EAAI Journal 2024 Journal Article

A distance and cosine similarity-based fitness evaluation mechanism for large-scale many-objective optimization

Cong Gao
Wenfeng Li
Lijun He
Lingchong Zhong

The fitness evaluation mechanism (FEM) based on nondominated sorting may lead to slow convergence when solving large-scale many-objective optimization problems (LSMaOPs), because the number of comparisons will become extremely large with the increase of optimization objectives and iterations. To solve this problem, a novel FEM based on distance and cosine similarity (DCS) is proposed in this paper. In each iteration, DCS needs to generate an ideal point after normalizing all objective functions. DCS consists of two important components, i. e. , the distance and cosine similarity. The distance similarity that mines the similar relationship between solutions and ideal point is calculated as the convergence measure, and the cosine similarity that reflects the uniformity of solution distribution is calculated as the diversity measure. Furthermore, DCS fuses the distance and cosine similarity into a comprehensive similarity to fully evaluate the quality of solutions. Both theoretical analysis and empirical results indicate that DCS has lower computational complexity than other state-of-the-art FEMs. To verify the performance of DCS in solving LSMaOPs, DCS and the competitors are respectively embedded in genetic algorithm, and then compared on 56 test instances with 5–15 objectives and 100–1000 decision variables. The experimental results show the effectiveness and superiority of DCS.

Details DOI

AAAI Conference 2023 Conference Paper

Multi-Modal Knowledge Hypergraph for Diverse Image Retrieval

Yawen Zeng
Qin Jin
Tengfei Bao
Wenfeng Li

The task of keyword-based diverse image retrieval has received considerable attention due to its wide demand in real-world scenarios. Existing methods either rely on a multi-stage re-ranking strategy based on human design to diversify results, or extend sub-semantics via an implicit generator, which either relies on manual labor or lacks explainability. To learn more diverse and explainable representations, we capture sub-semantics in an explicit manner by leveraging the multi-modal knowledge graph (MMKG) that contains richer entities and relations. However, the huge domain gap between the off-the-shelf MMKG and retrieval datasets, as well as the semantic gap between images and texts, make the fusion of MMKG difficult. In this paper, we pioneer a degree-free hypergraph solution that models many-to-many relations to address the challenge of heterogeneous sources and heterogeneous modalities. Specifically, a hyperlink-based solution, Multi-Modal Knowledge Hyper Graph (MKHG) is proposed, which bridges heterogeneous data via various hyperlinks to diversify sub-semantics. Among them, a hypergraph construction module first customizes various hyperedges to link the heterogeneous MMKG and retrieval databases. A multi-modal instance bagging module then explicitly selects instances to diversify the semantics. Meanwhile, a diverse concept aggregator flexibly adapts key sub-semantics. Finally, several losses are adopted to optimize the semantic space. Extensive experiments on two real-world datasets have well verified the effectiveness and explainability of our proposed method.

PDF Details DOI

EAAI Journal 2022 Journal Article

A digital implantation system for Z-direction yarn of three-dimensional preform based on flexible oriented woven process

Zitong Guo
Hao Huang
Zhongde Shan
Jihua Huang
Zhuojian Hou
Wenfeng Li

The implantation of Z-direction yarn is crucial in the process of forming flexible oriented 3D woven preforms, but manual implantation of Z-direction yarn is both time-consuming and inefficient. In this study, the digital Z-direction yarn implantation process and device are developed. Firstly, in view of many targets, small diameter, and serious interference in the identification process of the guide sleeve, a modified YOLOv3 algorithm is proposed to improve the accuracy and speed of detection, completing the coarse identification of guide sleeve. Then, an algorithm with modified Hough transform is proposed to improve the accuracy and speed of circle detection of coarse identification guide sleeve. Finally, based on the 1. 2 mm diameter guide sleeve, a digital yarn replacement device was built to precisely replace the guide sleeve with replacement needles and implant the Z-direction yarn into the preform. The identification error of the guide sleeve is within 0. 31% and the error between the reconstructed coordinates and the actual coordinates is within 3. 08%. This study is crucial to the identification and positioning of small targets under complex working conditions and the development of digital forming of preform.

Details DOI