Fanyou Wu | 吴凡优

Selected publications

1. 🚥

   Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform

   Yang Liu, Fanyou Wu, Cheng Lyu, Shen Li, Jiepin Ye, Xiaobo Qu

   Transportation Research Part E 2022

   bibtex abstract HTML

   @article{liu2022learning,
     title = {Deep dispatching: A deep reinforcement learning approach for vehicle dispatching on online ride-hailing platform},
     author = {Liu, Yang and Wu, Fanyou and Lyu, Cheng and Li, Shen and Ye, Jiepin and Qu, Xiaobo},
     journal = {Transportation Research Part E },
     year = {2022},
     doi = {10.1016/j.tre.2022.102694}
   }

   The vehicle dispatching system is one of the most critical problems in online taxi-hailing platforms, which requires adapting the operation and management strategy to the dynamics of demand and supply. In this paper, we propose a single-agent deep reinforcement learning approach for the vehicle repositioning problem by reallocating vacant vehicles to regions with a large demand gap in advance. The simulator and the vehicle repositioning algorithm are designed based on industrial-scale real-world data and the workflow of online taxi-hailing platforms, ensuring the practical value of our approach. Besides, the vehicle repositioning problem is translated in analogy with the load balancing problem in computers. Inspired by the recommendation system, the high concurrency of repositioning requests is addressed by sorting the actions as a recommendation list, whereby matching action with requests. Experiments demonstrate that the proposed approach is superior to the existing ones. It is also worth noting that the proposed approach won first place in the vehicle repositioning task of KDD Cup 2020.

2. 🌳

   Deep BarkID: A Portable Tree Bark Identification System by Knowledge Distillation

   Fanyou Wu, Rado Gazo, Bedrich Benes, Eva Haviarova

   European Journal of Forest Research 2021

   bibtex abstract HTML Code

   @article{wu2021bark,
     title = {Deep BarkID: A Portable Tree Bark Identification System by Knowledge Distillation},
     author = {Wu, Fanyou and Gazo, Rado and Benes, Bedrich and Haviarova, Eva},
     journal = {European Journal of Forest Research},
     year = {2021},
     doi = {10.1007/s10342-021-01407-7}
   }

   Species identification is one of the key steps in the management and conservation planning of many forest ecosystems. We introduce Deep BarkID, a portable tree identification system that detects tree species from bark images. Existing bark identification systems rely heavily on massive computing power access, which may be scarce in many locations. Our approach is deployed as a smartphone application that does not require any connection to a database. Its intended use is in a forest, where internet connection is often unavailable. The tree bark identification is expressed as a bark image classification task, and it is implemented as a convolutional neural network (CNN). This research focuses on developing light-weight CNN models through knowledge distillation. Overall, we achieved 96.12% accuracy for tree species classification tasks for ten common tree species in Indiana, USA. We also captured and prepared thousands of bark images—a dataset that we call Indiana Bark Dataset—and we make it available at https://github.com/wufanyou/DBID.

3. 🌳

   Wood Identification Based on Longitudinal Section Images by Using Deep Learning

   Fanyou Wu, Rado Gazo, Eva Haviarova, Bedrich Benes

   Wood Science and Technology 2021

   bibtex abstract HTML Code

   @article{wu2021wood,
     title = {Wood Identification Based on Longitudinal Section Images by Using Deep Learning},
     author = {Wu, Fanyou and Gazo, Rado and Haviarova, Eva and Benes, Bedrich},
     journal = {Wood Science and Technology},
     year = {2021},
     doi = {10.1007/s00226-021-01261-1},
     pages = {553-563},
     volume = {55},
     number = {2}
   }

   Automatic species identification has the potential to improve the efficacy and automation of wood processing systems significantly. Recent advances in deep learning allowed for the automation of many previously difficult tasks, and in this paper, we investigate the feasibility of using Deep Convolutional Neural Networks (CNNs) for hardwood lumber identification. In particular, we tested two highly effective CNNs (ResNet-50 and DenseNet-121) as well as lightweight MobileNet-V2. Overall, we achieved 98.2% accuracy for 11 common hardwood species classification tasks.

4. 📷

   Efficient Project Gradient Descent for Ensemble Adversarial Attack

   Fanyou Wu, Rado Gazo, Eva Haviarova, Bedrich Benes

   arXiv preprint arXiv:1906.03333 2019

   bibtex abstract arXiv Code

   @article{wu2019efficient,
     title = {Efficient Project Gradient Descent for Ensemble Adversarial Attack},
     author = {Wu, Fanyou and Gazo, Rado and Haviarova, Eva and Benes, Bedrich},
     journal = {arXiv preprint arXiv:1906.03333},
     arxiv = {1906.03333},
     year = {2019}
   }

   Recent advances show that deep neural networks are not robust to deliberately crafted adversarial examples which many are generated by adding human imperceptible perturbation to clear input. Consider l2 norms attacks, Project Gradient Descent (PGD) and the Carlini and Wagner (C&W) attacks are the two main methods, where PGD control max perturbation for adversarial examples while C&W approach treats perturbation as a regularization term optimized it with loss function together. If we carefully set parameters for any individual input, both methods become similar. In general, PGD attacks perform faster but obtains larger perturbation to find adversarial examples than the C&W when fixing the parameters for all inputs. In this report, we propose an efficient modified PGD method for attacking ensemble models by automatically changing ensemble weights and step size per iteration per input. This method generates smaller perturbation adversarial examples than PGD method while remains efficient as compared to C&W method. Our method won the first place in IJCAI19 Targeted Adversarial Attack competition.