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Improving Air Mobility for Pre-Disaster Planning with Neural Network Accelerated Genetic Algorithm

Autor: Acharya, Kamal, Velasquez, Alvaro, Liu, Yongxin, Liu, Dahai, Sun, Liang, Song, Houbing

Weather disaster related emergency operations pose a great challenge to air mobility in both aircraft and airport operations, especially when the impact is gradually approaching. We propose an optimized framework for adjusting airport operational sch

Externí odkaz: http://arxiv.org/abs/2408.00790

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Explainable AI for Comparative Analysis of Intrusion Detection Models

Autor: Corea, Pap M., Liu, Yongxin, Wang, Jian, Niu, Shuteng, Song, Houbing

Explainable Artificial Intelligence (XAI) has become a widely discussed topic, the related technologies facilitate better understanding of conventional black-box models like Random Forest, Neural Networks and etc. However, domain-specific application

Externí odkaz: http://arxiv.org/abs/2406.09684

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GraphDAC: A Graph-Analytic Approach to Dynamic Airspace Configuration

Autor: Feng, Ke, Liu, Dahai, Liu, Yongxin, Liu, Hong, Song, Houbing

The current National Airspace System (NAS) is reaching capacity due to increased air traffic, and is based on outdated pre-tactical planning. This study proposes a more dynamic airspace configuration (DAC) approach that could increase throughput and

Externí odkaz: http://arxiv.org/abs/2307.15876

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Extended ADMM for general penalized quantile regression with linear constraints in big data

Autor: Liu, Yongxin, Zeng, Peng

Quantile regression (QR) can be used to describe the comprehensive relationship between a response and predictors. Prior domain knowledge and assumptions in application are usually formulated as constraints of parameters to improve the estimation eff

Externí odkaz: http://arxiv.org/abs/2305.07481

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NoiseCAM: Explainable AI for the Boundary Between Noise and Adversarial Attacks

Autor: Tan, Wenkai, Renkhoff, Justus, Velasquez, Alvaro, Wang, Ziyu, Li, Lusi, Wang, Jian, Niu, Shuteng, Yang, Fan, Liu, Yongxin, Song, Houbing

Deep Learning (DL) and Deep Neural Networks (DNNs) are widely used in various domains. However, adversarial attacks can easily mislead a neural network and lead to wrong decisions. Defense mechanisms are highly preferred in safety-critical applicatio

Externí odkaz: http://arxiv.org/abs/2303.06151

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Exploring Adversarial Attacks on Neural Networks: An Explainable Approach

Autor: Renkhoff, Justus, Tan, Wenkai, Velasquez, Alvaro, Wang, illiam Yichen, Liu, Yongxin, Wang, Jian, Niu, Shuteng, Fazlic, Lejla Begic, Dartmann, Guido, Song, Houbing

Deep Learning (DL) is being applied in various domains, especially in safety-critical applications such as autonomous driving. Consequently, it is of great significance to ensure the robustness of these methods and thus counteract uncertain behaviors

Externí odkaz: http://arxiv.org/abs/2303.06032

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