Dr. Ting Zhang Profile

Dr. Ting Zhang

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Proceedings Article | 11 October 2023 Paper

AHFL-UAVs: adaptive loss isomorphism federated learning algorithm for UAV swarms

Fanliang Meng, Xiaoguang Nie, Juan Gu, Shengyu Jia, Ting Zhang, Yujian Li

Proceedings Volume 12800, 128001B (2023) https://doi.org/10.1117/12.3004027

KEYWORDS: Education and training, Machine learning, Unmanned aerial vehicles, Data privacy, Evolutionary algorithms, Computer security, Deep learning, Neural networks, Network security, Mathematical modeling

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UAV swarms have crucial applications in modern military, geological exploration, and 5G/6G communication fields. As communication nodes, drones frequently exchange wireless data with other drones, and data privacy protection is currently one of the most urgent research topics. Based on this, this article proposes an efficient isomorphic federated learning algorithm for unmanned aerial vehicle clusters. First, set the adaptive loss differential adaptive parameter, with the initial value set to a floating point number not greater than 0.01, then activate it with the Tanh function, participate in the training as part of the loss function during the training process, and optimize it by gradient descent. When the UAV node uploads the gradient to the wingman, sum the adaptive loss differential parameter with the gradient matrix as a disturbance term. Then, based on the dynamic confidence matrix, high-quality drones are selected to participate in the gradient security aggregation of drones, achieving the selection of high-quality drones. The built-in global neural network of the drone transmits shared parameters indiscriminately to each drone through broadcast to achieve updates to the shared parameters. The comparative experiments of our algorithm on the Fashion and Cifar10 datasets show that our algorithm has higher accuracy, with the highest accuracy improvement of 4.42% on the Mnist dataset and 8.22% on the Cifar10 dataset.

Proceedings Article | 12 October 2022 Paper

Ship target detection based on multitask learning

Junran He, Ting Zhang, Zhaoying Liu, Yujian Li

Proceedings Volume 12342, 123420H (2022) https://doi.org/10.1117/12.2644430

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Ship target detection is of great significance in marine surveillance, rescue and so on. In this paper, in order to improve the performance of ship target detection, we proposed a ship target detection method based on multi task learning. There are mainly two contributions. Firstly, we designed a multi-task learning model by integrating segmentation module to the faster RCNN model. Through the strategies of feature sharing and joint learning, it is helpful to improve the accuracy of target detection with the assistance of segmentation; Secondly, in order to deal with the impact of initial anchor frame scale on target detection accuracy, we introduced an adaptive anchor width height ratio setting method based on improved K-means algorithm, by adaptively select initial anchor size suitable for the characteristics of ship targets, it is beneficial to further improve the detection accuracy. Moreover, we constructed an extended version of ship image data set including 14614 images belonging to 13 categories. Experimental results demonstrated that the proposed model can effectively improve the accuracy of ship target detection; and the comparison and the ablation experiments further validated the strategies of multi-task joint learning and adaptive anchor size setting is helpful for improving the performance of ship target detection.

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