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Bai Li

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5 papers

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EAAI Journal 2023 Journal Article

Autonomous dispatch trajectory planning on flight deck: A search-resampling-optimization framework

Xinwei Wang
Bai Li
Xichao Su
Haijun Peng
Lei Wang
Chen Lu
Chao Wang

There is a growing expectation to realize the autonomous dispatch on flight deck, where dispatch trajectory planning is seen as the key technique. Optimal-control based method has shown great advantages in high degree of constraint satisfaction over its counterparts in the last decade. However, it suffers from low computational efficiency even numerical divergence under scenarios with complicated obstacles. To deal with such an issue, a search-resampling-optimization (SRO) framework is proposed in this paper. A hybrid A* algorithm is employed to generate a coarse path according to the boundary conditions in the search stage. Then a resampling process is implemented to pave a series of safe dispatch corridors (SDCs) along the coarse path. Finally, by replacing the common one-to-one collision-avoidance with the constructed within-SDC constraints, an optimal control problem whose scale is totally independent of the number of obstacles can be formulated. The resampled result is further fed into the optimization stage to facilitate the numerical solution. Dispatch trajectory planning for taxiing aircraft and tractor can be treated uniformly under this framework. And numerical simulations demonstrate that the SRO framework is efficient and robust even with narrow accessible tunnels. The SRO is inherently flexible and can be easily extended to the trajectory planning problem in other fields. A video of the main idea and numerical simulations in this paper is available at www. bilibili. com/video/BV1tP4y1d7xy/.

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AAAI Conference 2020 Conference Paper

Graph-Driven Generative Models for Heterogeneous Multi-Task Learning

Wenlin Wang
Hongteng Xu
Zhe Gan
Bai Li
Guoyin Wang
Liqun Chen
Qian Yang
Wenqi Wang

We propose a novel graph-driven generative model, that uniﬁes multiple heterogeneous learning tasks into the same framework. The proposed model is based on the fact that heterogeneous learning tasks, which correspond to different generative processes, often rely on data with a shared graph structure. Accordingly, our model combines a graph convolutional network (GCN) with multiple variational autoencoders, thus embedding the nodes of the graph (i. e. , samples for the tasks) in a uniform manner, while specializing their organization and usage to different tasks. With a focus on healthcare applications (tasks), including clinical topic modeling, procedure recommendation and admission-type prediction, we demonstrate that our method successfully leverages information across different tasks, boosting performance in all tasks and outperforming existing state-of-the-art approaches.

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NeurIPS Conference 2019 Conference Paper

Certified Adversarial Robustness with Additive Noise

Bai Li
Changyou Chen
Wenlin Wang
Lawrence Carin

The existence of adversarial data examples has drawn significant attention in the deep-learning community; such data are seemingly minimally perturbed relative to the original data, but lead to very different outputs from a deep-learning algorithm. Although a significant body of work on developing defense models has been developed, most such models are heuristic and are often vulnerable to adaptive attacks. Defensive methods that provide theoretical robustness guarantees have been studied intensively, yet most fail to obtain non-trivial robustness when a large-scale model and data are present. To address these limitations, we introduce a framework that is scalable and provides certified bounds on the norm of the input manipulation for constructing adversarial examples. We establish a connection between robustness against adversarial perturbation and additive random noise, and propose a training strategy that can significantly improve the certified bounds. Our evaluation on MNIST, CIFAR-10 and ImageNet suggests that our method is scalable to complicated models and large data sets, while providing competitive robustness to state-of-the-art provable defense methods.

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EAAI Journal 2014 Journal Article

An edge-based optimization method for shape recognition using atomic potential function

Bai Li
Yuan Yao

The edge potential function (EPF) approach is a promising edge-based shape matching tool for visual target recognition, and describes the similarity between contours by means of a potential field. However, background noise in test images may degrade the accuracy of the EPF approach in the identification of target contours. Furthermore, the computational load of the EPF approach is usually heavy, thus limiting its use in online applications. To solve these problems, this paper proposes a new shape matching tool based on atomic potential function (APF). The APF approach reduces the effects of background noise by introducing the concept of atom potential to the generation of potential fields. Moreover, in our proposed APF approach, the potential field is calculated using the contour extracted from a pre-defined target template rather than contours extracted from test images. Following the calculation of the potential field, the derived potential field is transformed to match the contours extracted from the test images. The search process for the transformation that matches the contours most closely is modeled as an optimization problem solved by a modified version of the artificial bee colony (ABC) algorithm – the internal feedback ABC (IF-ABC). Compared to the conventional ABC algorithm, IF-ABC effectively avoids premature convergence and significantly improves convergence speed. Experimental results verify the feasibility and efficiency of our proposed APF approach by comparing it with the traditional EPF method.

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EAAI Journal 2014 Journal Article

Protein secondary structure optimization using an improved artificial bee colony algorithm based on AB off-lattice model

Bai Li
Ya Li
Ligang Gong

Predicting the secondary structure of protein has been the focus of scientific research for decades, but it remains to be a challenge in bioinformatics due to the increasing computation complexity. In this paper, AB off-lattice model is introduced to transforms the prediction task into a numerical optimization problem. Artificial Bee Colony algorithm (ABC) is an effective swarm intelligence algorithm, which works well in exploration but poor at exploitation. To improve the convergence performance of ABC, a novel internal feedback strategy based ABC (IF-ABC) is proposed. In this strategy, internal states are fully used in each of the iterations to guide subsequent searching process, and to balance local exploration with global exploitation. We provide the mechanism together with the convergence proof of the modified algorithm. Simulations are conducted on artificial Fibonacci sequences and real sequences in the database of Protein Data Bank (PDB). The analysis implies that IF-ABC is more effective to improve convergence rate than ABC, and can be employed for this specific protein structure prediction issues.

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