Yirui Wang

IROS Conference 2025 Conference Paper

Pseudo Depth Meets Gaussian: A Feed-forward RGB SLAM Baseline

• Linqing Zhao
• Xiuwei Xu
• Yirui Wang
• Hao Wang
• Wenzhao Zheng
• Yansong Tang
• Haibin Yan
• Jiwen Lu

EAAI Journal 2023 Journal Article

An adaptive position-guided gravitational search algorithm for function optimization and image threshold segmentation

• Anjing Guo
• Yirui Wang
• Lijun Guo
• Rong Zhang
• Yang Yu
• Shangce Gao

Gravitational search algorithm is a population-based optimization method. To address its low search performance and premature convergence, a novel variant called adaptive position-guided gravitational search algorithm is proposed. It utilizes the best, worst and other particles’ position information to adaptively determine the Kbest particles which provide a good movement direction. The gravitational force is reinforced by Kbest particles and new constructed Dbest particles to improve the exploration and exploitation abilities. Various particles’ position information jointly provide the effective search guideline and accelerate the convergence rate. Validations are conducted to firstly discuss the parameters and strategies of the proposed algorithm. Then, compared with several state-of-the-art gravitational search algorithm variants on CEC2017 benchmark functions, the proposed algorithm proves its superiority. Finally, the proposed algorithm exhibits the good segmentation effect on image threshold segmentation problems.

EAAI Journal 2023 Journal Article

An improved spherical evolution with enhanced exploration capabilities to address wind farm layout optimization problem

• Haichuan Yang
• Shangce Gao
• Zhenyu Lei
• Jiayi Li
• Yang Yu
• Yirui Wang

The utilization of metaheuristics for optimizing wind farm layouts (WFLOP) has emerged as a popular research area in recent years. However, effectively screening and improving metaheuristics to obtain optimal layouts remain a challenging task. Traditional metaheuristic screening methods require testing numerous algorithms, resulting in high computational resource consumption and trial-and-error costs due to the lack of theoretical guidance. To overcome this challenge, this study proposes a complex network-based metaheuristic screening method. Population interaction networks are utilized to classify metaheuristics into two categories: biased exploitation and biased exploration. The results of several metaheuristics on WFLOP suggest that exploration-biased algorithms generally outperform exploitation-biased ones. This discovery holds great significance as it has the potential to predict the performance of various algorithms on WFLOP to a certain degree. Additionally, it provides valuable suggestions for algorithm selection and improvement. Building upon this new methodology, we screen and improve the spherical evolution algorithm to enhance its exploration capabilities. Experimental results demonstrate that the improved spherical evolution algorithm significantly outperforms its competitors on WFLOP.

AAAI Conference 2021 Conference Paper

Window Loss for Bone Fracture Detection and Localization in X-ray Images with Point-based Annotation

• Xinyu Zhang
• Yirui Wang
• Chi-Tung Cheng
• Le Lu
• Adam P. Harrison
• Jing Xiao
• Chien-Hung Liao
• Shun Miao

Object detection methods are widely adopted for computeraided diagnosis using medical images. Anomalous findings are usually treated as objects that are described by bounding boxes. Yet, many pathological findings, e. g. , bone fractures, cannot be clearly defined by bounding boxes, owing to considerable instance, shape and boundary ambiguities. This makes bounding box annotations, and their associated losses, highly ill-suited. In this work, we propose a new bone fracture detection method for X-ray images, based on a labor effective and flexible annotation scheme suitable for abnormal findings with no clear object-level spatial extents or boundaries. Our method employs a simple, intuitive, and informative pointbased annotation protocol to mark localized pathology information. To address the uncertainty in the fracture scales annotated via point(s), we convert the annotations into pixel-wise supervision that uses lower and upper bounds with positive, negative, and uncertain regions. A novel Window Loss is subsequently proposed to only penalize the predictions outside of the uncertain regions. Our method has been extensively evaluated on 4410 pelvic X-ray images of unique patients. Experiments demonstrate that our method outperforms previous state-of-the-art image classification and object detection baselines by healthy margins, with an AUROC of 0. 983 and FROC score of 89. 6%.