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602 results on '"You, Xiong"'

1. Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images

Author

Gao, Kuiliang, Yu, Anzhu, You, Xiong, Guo, Wenyue, Li, Ke, and Huang, Ningbo

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

In the existing unsupervised domain adaptation (UDA) methods for remote sensing images (RSIs) semantic segmentation, class symmetry is an widely followed ideal assumption, where the source and target RSIs have exactly the same class space. In practice, however, it is often very difficult to find a source RSI with exactly the same classes as the target RSI. More commonly, there are multiple source RSIs available. To this end, a novel class asymmetry RSIs domain adaptation method with multiple sources is proposed in this paper, which consists of four key components. Firstly, a multi-branch segmentation network is built to learn an expert for each source RSI. Secondly, a novel collaborative learning method with the cross-domain mixing strategy is proposed, to supplement the class information for each source while achieving the domain adaptation of each source-target pair. Thirdly, a pseudo-label generation strategy is proposed to effectively combine strengths of different experts, which can be flexibly applied to two cases where the source class union is equal to or includes the target class set. Fourthly, a multiview-enhanced knowledge integration module is developed for the high-level knowledge routing and transfer from multiple domains to target predictions., Comment: 17 pages, 10 figures

Published
2023

3. Exponentially fitted two-derivative DIRK methods for oscillatory differential equations

Author

Ehigie, Julius O., Luan, Vu Thai, Okunuga, Solomon A., and You, Xiong

Subjects
Mathematics - Numerical Analysis, 65L05, 65L12, 65L10
Abstract

In this work, we construct and derive a new class of exponentially fitted two-derivative diagonally implicit Runge--Kutta (EFTDDIRK) methods for the numerical solution of differential equations with oscillatory solutions. First, a general format of so-called modified two-derivative diagonally implicit Runge--Kutta methods (TDDIRK) is proposed. Their order conditions up to order six are derived by introducing a set of bi-coloured rooted trees and deriving new elementary weights. Next, we build exponential fitting conditions in order for these modified TDDIRK methods to treat oscillatory solutions, leading to EFTDDIRK methods. In particular, a family of 2-stage fourth-order, a fifth-order, and a 3-stage sixth-order EFTDDIRK schemes are derived. These can be considered as superconvergent methods. The stability and phase-lag analysis of the new methods are also investigated, leading to optimized fourth-order schemes, which turn out to be much more accurate and efficient than their non-optimized versions. Finally, we carry out numerical experiments on some oscillatory test problems. Our numerical results clearly demonstrate the accuracy and efficiency of the newly derived methods when compared with existing implicit Runge--Kutta methods and two-derivative Runge--Kutta methods of the same order in the literature., Comment: 44 pages, 11 figures

Published
2020

7. Transcriptomic and Proteomic Landscape of Sugarcane Response to Biotic and Abiotic Stressors

Author

Ao-Mei Li, Fen Liao, Miao Wang, Zhong-Liang Chen, Cui-Xian Qin, Ruo-Qi Huang, Krishan K. Verma, Yang-Rui Li, You-Xiong Que, You-Qiang Pan, and Dong-Liang Huang

Subjects
sugarcane, transcriptome, proteome, gene, miRNA, stress, Biology (General), QH301-705.5, Chemistry, QD1-999
Abstract

Sugarcane, a C4 plant, provides most of the world’s sugar, and a substantial amount of renewable bioenergy, due to its unique sugar-accumulating and feedstock properties. Brazil, India, China, and Thailand are the four largest sugarcane producers worldwide, and the crop has the potential to be grown in arid and semi-arid regions if its stress tolerance can be improved. Modern sugarcane cultivars which exhibit a greater extent of polyploidy and agronomically important traits, such as high sugar concentration, biomass production, and stress tolerance, are regulated by complex mechanisms. Molecular techniques have revolutionized our understanding of the interactions between genes, proteins, and metabolites, and have aided in the identification of the key regulators of diverse traits. This review discusses various molecular techniques for dissecting the mechanisms underlying the sugarcane response to biotic and abiotic stresses. The comprehensive characterization of sugarcane’s response to various stresses will provide targets and resources for sugarcane crop improvement.

Published
2023
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15. BiCOD: A Camouflaged Object Detection Method Directed by Cognitive Attention

Author

Xu, Lianrui, You, Xiong, Jia, Fenli, and Liu, Kangyu

Abstract

Camouflaged object detection (COD) is a typical application of deep-coupled unmanned platform combat support, which aims to detect objects that are highly similar to the background in terms of structure, details, and texture while improving the efficiency and accuracy of detecting camouflaged objects. The existing COD methods are built upon extraction and segmentation of image features and lack of theoretical interpretability. In this article, the task of COD was revisited and analyzed. From the perspective of cognition, the cognitive laws of camouflaged objects were assessed through eye movement experiments to form an entire cognitive process, which serves as a guide for designing COD methods. Feature extraction, position attention, and channel attention modules (CAMs) were utilized as the basic framework. The residual-in-residual module was introduced to improve the accuracy of feature learning and transmission. Then, a bidirectional attention module (BAM) was added to guide the feedforward and feedback of attention features, and a closed loop was formed to achieve efficient feature transmission and use. As a result, the performance of our method BiCOD was promoted. In addition, a COD dataset containing both natural and artificial camouflage objects was compiled to evaluate the generalization ability of the camouflaged object recognition algorithm. The experimental results showed that BiCOD achieved an advanced level in quantitative results and visual comparisons in general, and the effectiveness and accuracy of the method in different environments were verified.

Published
2024
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20. Research on the Utilization of Metro Regenerative Braking Energy Based on an Improved Differential Evolution Algorithm

Author

Di Liu, Song-Qing Zhu, Yun-Rui Bi, Kun Liu, and You-Xiong Xu

Subjects
Transportation engineering, TA1001-1280, Transportation and communications, HE1-9990
Abstract

Urban metro trains have the characteristics of short running distance between stations and frequent starting and braking. A large amount of regenerative braking energy is generated during the braking process. The effective utilization of the regenerative braking energy can substantially reduce the total energy consumption of train operation. In this paper, we establish two integer programming models of train operation that maximize the overlap time between train traction and braking in peak hours and nonpeak hours. On this basis, an improved differential evolution (IDE) algorithm is developed for solving the two integer programming models. The results demonstrate that the overlap time increases by 51.44% after optimization using the IDE algorithm when the headway is set to 154 s in peak hours. The overlap time is further increased by 14.87% by optimizing the headway. In nonpeak hours, the overlap time of traction and braking of the trains in opposite directions at the same station is increased by optimizing the bidirectional departure interval, thereby reducing the total energy consumption of the system.

Published
2020
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25. Integrating Multiple Sources Knowledge for Class Asymmetry Domain Adaptation Segmentation of Remote Sensing Images

Author

Gao, Kuiliang, Yu, Anzhu, You, Xiong, Guo, Wenyue, Li, Ke, and Huang, Ningbo

Abstract

In the existing unsupervised domain adaptation (UDA) methods for remote sensing images (RSIs) semantic segmentation, class symmetry is a widely followed ideal assumption, where the source and target RSIs have exactly the same class space. In practice, however, it is often very difficult to find a source RSI with exactly the same classes as the target RSI. More commonly, there are multiple source RSIs available. And there is always an intersection or inclusion relationship between the class spaces of each source–target pair, which can be referred to as class asymmetry. Nevertheless, the class asymmetry domain adaptation segmentation of RSIs with multiple sources has not yet been explored. To this end, a novel class asymmetry RSIs domain adaptation method is proposed for the first time in this article, which consists of four key components. First, a multibranch segmentation network is built to learn an expert for each source RSI. Second, a novel collaborative learning method with the cross-domain mixing strategy is proposed, to supplement the class information for each source while achieving the domain adaptation of each source–target pair. Third, a pseudolabel generation strategy is proposed to effectively combine the strengths of different experts, which can be flexibly applied to two cases where the source class union is equal to or includes the target class set. Fourth, a multiview-enhanced knowledge integration module is developed for high-level knowledge routing and transfer from multiple domains to target predictions. The experimental results of six different class settings on airborne and spaceborne RSIs show that the proposed method can effectively perform the multisource domain adaptation in the case of class asymmetry, and the obtained segmentation performance of target RSIs is significantly better than the existing relevant methods.

Published
2024
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50. Research on the Utilization of Metro Regenerative Braking Energy Based on an Improved Differential Evolution Algorithm

Author

Liu, Di, Zhu, Song-Qing, Bi, Yun-Rui, Liu, Kun, and Xu, You-Xiong

Subjects
Algorithms -- Analysis, Energy consumption -- Analysis, Algorithm, Transportation industry
Abstract

Urban metro trains have the characteristics of short running distance between stations and frequent starting and braking. A large amount of regenerative braking energy is generated during the braking process. The effective utilization of the regenerative braking energy can substantially reduce the total energy consumption of train operation. In this paper, we establish two integer programming models of train operation that maximize the overlap time between train traction and braking in peak hours and nonpeak hours. On this basis, an improved differential evolution (IDE) algorithm is developed for solving the two integer programming models. The results demonstrate that the overlap time increases by 51.44% after optimization using the IDE algorithm when the headway is set to 154 s in peak hours. The overlap time is further increased by 14.87% by optimizing the headway. In nonpeak hours, the overlap time of traction and braking of the trains in opposite directions at the same station is increased by optimizing the bidirectional departure interval, thereby reducing the total energy consumption of the system., 1. Introduction Urban metro transportation is safe, comfortable, punctual, and energy-conserving. It is the main public transport mode among various large- and medium-sized cities. As the mileage of metro construction [...]

Published
2020
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