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3. An Efficient B-spline-Based Kinodynamic Replanning Framework for Quadrotors

Author

Ding, Wenchao, Gao, Wenliang, Wang, Kaixuan, and Shen, Shaojie

Subjects
Computer Science - Robotics, Computer Science - Artificial Intelligence
Abstract

Trajectory replanning for quadrotors is essential to enable fully autonomous flight in unknown environments. Hierarchical motion planning frameworks, which combine path planning with path parameterization, are popular due to their time efficiency. However, the path planning cannot properly deal with non-static initial states of the quadrotor, which may result in non-smooth or even dynamically infeasible trajectories. In this paper, we present an efficient kinodynamic replanning framework by exploiting the advantageous properties of the B-spline, which facilitates dealing with the non-static state and guarantees safety and dynamical feasibility. Our framework starts with an efficient B-spline-based kinodynamic (EBK) search algorithm which finds a feasible trajectory with minimum control effort and time. To compensate for the discretization induced by the EBK search, an elastic optimization (EO) approach is proposed to refine the control point placement to the optimal location. Systematic comparisons against the state-of-the-art are conducted to validate the performance. Comprehensive onboard experiments using two different vision-based quadrotors are carried out showing the general applicability of the framework., Comment: IEEE Transactions on Robotics

Published
2019

4. Trajectory Replanning for Quadrotors Using Kinodynamic Search and Elastic Optimization

Author

Ding, Wenchao, Gao, Wenliang, Wang, Kaixuan, and Shen, Shaojie

Subjects
Computer Science - Robotics
Abstract

We focus on a replanning scenario for quadrotors where considering time efficiency, non-static initial state and dynamical feasibility is of great significance. We propose a real-time B-spline based kinodynamic (RBK) search algorithm, which transforms a position-only shortest path search (such as A* and Dijkstra) into an efficient kinodynamic search, by exploring the properties of B-spline parameterization. The RBK search is greedy and produces a dynamically feasible time-parameterized trajectory efficiently, which facilitates non-static initial state of the quadrotor. To cope with the limitation of the greedy search and the discretization induced by a grid structure, we adopt an elastic optimization (EO) approach as a post-optimization process, to refine the control point placement provided by the RBK search. The EO approach finds the optimal control point placement inside an expanded elastic tube which represents the free space, by solving a Quadratically Constrained Quadratic Programming (QCQP) problem. We design a receding horizon replanner based on the local control property of B-spline. A systematic comparison of our method against two state-of-the-art methods is provided. We integrate our replanning system with a monocular vision-based quadrotor and validate our performance onboard., Comment: 8 pages. Published in International Conference on Robotics and Automation (ICRA) 2018. IEEE copyright

Published
2019
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22. 2D Semantic Segmentation: Recent Developments and Future Directions.

Author

Guo, Yu, Nie, Guigen, Gao, Wenliang, and Liao, Mi

Subjects
DEEP learning, COMPUTER vision, IMAGE recognition (Computer vision), CONVOLUTIONAL neural networks, IMAGE segmentation
Abstract

Semantic segmentation is a critical task in computer vision that aims to assign each pixel in an image a corresponding label on the basis of its semantic content. This task is commonly referred to as dense labeling because it requires pixel-level classification of the image. The research area of semantic segmentation is vast and has achieved critical advances in recent years. Deep learning architectures in particular have shown remarkable performance in generating high-level, hierarchical, and semantic features from images. Among these architectures, convolutional neural networks have been widely used to address semantic segmentation problems. This work aims to review and analyze recent technological developments in image semantic segmentation. It provides an overview of traditional and deep-learning-based approaches and analyzes their structural characteristics, strengths, and limitations. Specifically, it focuses on technical developments in deep-learning-based 2D semantic segmentation methods proposed over the past decade and discusses current challenges in semantic segmentation. The future development direction of semantic segmentation and the potential research areas that need further exploration are also examined. [ABSTRACT FROM AUTHOR]

Published
2023
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23. Notch3 Transactivates Glycogen Synthase Kinase-3-Beta and Inhibits Epithelial-to-Mesenchymal Transition in Breast Cancer Cells

Author

Chen, Weiling, primary, Zhang, Yongqu, additional, Li, Ronghui, additional, Huang, Wenhe, additional, Wei, Xiaolong, additional, Zeng, De, additional, Liang, Yuanke, additional, Zeng, Yunzhu, additional, Chen, Min, additional, Zhang, Lixin, additional, Gao, Wenliang, additional, Zhu, Yuanyuan, additional, Li, Yaochen, additional, and Zhang, Guojun, additional

Published
2022
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33. The prognostic factors and efficacy of induction chemotherapy in elderly patients with acute myeloid leukemia

Author

Mao, Jianping, Gao, Wenliang, Xue, Lianguo, Zhao, Lidong, Miao, Lei, Jia, Tao, Zhu, Yuanxin, Wang, Ying, Meng, Lijuan, and Wang, Juan

Subjects
Original Article
Abstract

Background and objective: Acute myeloid leukemia (AML) is the most common form of hematological malignancy in adults. We aimed to investigate the efficacy of different treatment measures and prognostic factors for elderly patients with AML. Methods: The clinical data of 65 newly diagnosed elderly patients with AML were retrospectively analyzed. Among them, 45 patients received induction chemotherapy including standard cytarabine regimen (n = 21) and low dose cytarabine regimen (n = 24), and 20 patients received palliative treatment. The efficacy and prognosis were compared between the groups. Results: There were no statistical differences in complete remission, overall survival and the 6-month disease-free survival rates between standard cytarabine group and low dose cytarabine group (P = 0.675, P = 0.775, P = 0.751, respectively). Significant difference in median overall survival and overall survival rate were detected (P < 0.001, P = 0.031, respectively), but no significant difference in early death rate (P = 0.238) was found between induction chemotherapy group and palliative treatment group. Multivariate analysis showed that the white blood cells count ≥ 100.0 × 10(9)/L was associated with early death. Conclusions: The induction chemotherapy did not increase the early mortality. The low dose cytarabine regimen can be used as the first-line choice for elderly acute myeloid leukemia patients who are not suitable for intensive chemotherapy.

Published
2020

43. SCMAG: A Semisupervised Single-Cell Clustering Method Based on Matrix Aggregation Graph Convolutional Neural Network.

Author

Gao, Wenliang, Li, Yuanyuan, Fang, Chujie, Fan, Wei, and Peng, Haonan

Subjects
*CONVOLUTIONAL neural networks, *ALGORITHMS, *DATA mining, *CLUSTER analysis (Statistics)
Abstract

Clustering analysis is one of the most important technologies for single-cell data mining. It is widely used in the division of different gene sequences, the identification of functional genes, and the detection of new cell types. Although the traditional unsupervised clustering method does not require label data, the distribution of the original data, the setting of hyperparameters, and other factors all affect the effectiveness of the clustering algorithm. While in some cases the type of some cells is known, it is hoped to achieve high accuracy if the prior information about those cells is utilized sufficiently. In this study, we propose SCMAG (a semisupervised single-cell clustering method based on a matrix aggregation graph convolutional neural network) that takes into full consideration the prior information for single-cell data. To evaluate the performance of the proposed semisupervised clustering method, we test on different single-cell datasets and compare with the current semisupervised clustering algorithm in recognizing cell types on various real scRNA-seq data; the results show that it is a more accurate and significant model. [ABSTRACT FROM AUTHOR]

Published
2021
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