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arXiv 2023年

作者： Sever, Gulay Goktas Demir, Umut Sadik Satir, A. Sahin, Mustafa Cagatay Ure, Nazım Kemal Istanbul Technical University Artificial Intelligence and Data Science Application and Research Center Istanbul34467 Turkey Turkish Aerospace Industries Inc. Ankara06980 Turkey

In this paper, we present a methodology for constructing data-driven maneuver generation models for agile aircraft that can generalize across a wide range of trim conditions and aircraft model parameters. Maneuver generation models play a crucial role in the testing and evaluation of aircraft prototypes, providing insights into the maneuverability and agility of the aircraft. However, constructing the models typically requires extensive amounts of real pilot data, which can be time-consuming and costly to obtain. Moreover, models built with limited data often struggle to generalize beyond the specific flight conditions covered in the original dataset. To address these challenges, we propose a hybrid architecture that leverages a simulation model, referred to as the source model. This open-source agile aircraft simulator shares similar dynamics with the target aircraft and allows us to generate unlimited data for building a proxy maneuver generation model. We then fine-tune this model to the target aircraft using a limited amount of real pilot data. Our approach combines techniques from imitation learning, transfer learning, and reinforcement learning to achieve this objective. To validate our methodology, we utilize real agile pilot data provided by Turkish Aerospace Industries (TAI). By employing the F-16 as the source model, we demonstrate that it is possible to construct a maneuver generation model that generalizes across various trim conditions and aircraft parameters without requiring any additional real pilot data. Our results showcase the effectiveness of our approach in developing robust and adaptable models for agile aircraft. © 2023, CC BY.

关键词： Reinforcement learning

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Cooperative Visual Inertial Odometry for Heterogeneous Swarm of Drones Navigating in Noisy Environments

Cooperative Visual Inertial Odometry for Heterogeneous Swarm...

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IEEE International Symposium on Industrial Electronics (ISIE)

作者： Kamil Canberk Atik Enes Erdogan Ahmet Yusuf Yahsi Feyzullah Kara Baris Yalcin Gü rkan Cetin Nazim Kemal Ure Faculty of Aeronautics and Astronautics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Computer and Informatics Engineering ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Faculty of Electrical and Electronics ITU Artificial Intelligence and Data Science Application and Research Center Istanbul Technical University Istanbul Turkey Havelsan Ankara Turkey

This paper presents a methodology for enhancing the localization performance of drone swarms by collaborative propagation of the estimates of drones equipped with higher cost sensors to the drones with lower-cost sensors. Although recent advances in visual-inertial odometry (VIO) enabled good performance in GPS denied environments, the localization performance is still highly dependent on the accuracy of inertial sensors. Moreover, environmental effects that corrupt visual inputs also contribute to the error rate of VIO estimates thus localization in noisy environments with low-grade sensors is still widely an open problem. We show that this problem can be alleviated when drones are navigating in the form of a swarm, and the relative distance measurements between drones are available through ultra-wideband (UWB) signals. In particular, we develop a collaborative Kalman filtering and optimization framework, where the drones with lower grade sensors receive updates from drones that have access to higher-quality measurements. We validate our approach in a simulation environment with realistic visual representations and show that the proposed methodology can significantly improve the localization performance, especially for the scenarios where the camera measurements are corrupted with high noise. We further present results in simplistic flight tests to demonstrate the applicability of our approach to real hardware.

关键词： Location awareness Visualization Costs Navigation Surveillance Teamwork Trajectory

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An Empirical Examination of Balancing Strategy for Counterfactual Estimation on Time Series 41

An Empirical Examination of Balancing Strategy for Counterfa...

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41st International Conference on Machine Learning, ICML 2024

作者： Huang, Qiang Meng, Chuizheng Cao, Defu Huang, Biwei Chang, Yi Liu, Yan School of Artificial Intelligence Jilin University Jilin Changchun China International Center of Future Science Jilin University Jilin Changchun China Department of Computer Science University of Southern California California Los Angeles United States Halicioğlu Data Science Institute University of California San Diego San DiegoCA United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MOE Jilin Changchun China

Counterfactual estimation from observations represents a critical endeavor in numerous application fields, such as healthcare and finance, with the primary challenge being the mitigation of treatment bias. The balancing strategy aimed at reducing covariate disparities between different treatment groups serves as a universal solution. However, when it comes to the time series data, the effectiveness of balancing strategies remains an open question, with a thorough analysis of the robustness and applicability of balancing strategies still lacking. This paper revisits counterfactual estimation in the temporal setting and provides a brief overview of recent advancements in balancing strategies. More importantly, we conduct a critical empirical examination for the effectiveness of the balancing strategies within the realm of temporal counterfactual estimation in various settings on multiple datasets. Our findings could be of significant interest to researchers and practitioners and call for a reexamination of the balancing strategy in time series settings. Copyright 2024 by the author(s)

关键词： Time series analysis

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Real-Time Proportional-Integral-Derivative(PID)Tuning Based on Back Propagation(BP)Neural Network for Intelligent Vehicle Motion Control

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Computers, Materials & Continua 2025年第5期83卷 2375-2401页

作者： Liang Zhou Qiyao Hu Xianlin Peng Qianlong Liu School of Information Science and Technology Northwest UniversityXi’an710127China State-Province Joint Engineering and Research Center of Advanced Networking and Intelligent Information Services Northwest UniversityXi’an710127China Generative Artificial Intelligence and Mixed Reality Key Laboratory of Higher Education Institutions in Shaanxi Province Xi’an710127China Shaanxi Silk Road Cultural Heritage Digital Protection and Inheritance Collaborative Innovation Center Xi’an710127China School of Art Northwest UniversityXi’an710127China Network and Data Center Northwest UniversityXi’an710127China

Over 1.3 million people die annually in traffic accidents,and this tragic fact highlights the urgent need to enhance the intelligence of traffic safety and control *** modern industrial and technological applications and collaborative edge intelligence,control systems are crucial for ensuring efficiency and ***,deficiencies in these systems can lead to significant operational *** paper uses edge intelligence to address the challenges of achieving target speeds and improving efficiency in vehicle control,particularly the limitations of traditional Proportional-Integral-Derivative(PID)controllers inmanaging nonlinear and time-varying dynamics,such as varying road conditions and vehicle behavior,which often result in substantial discrepancies between desired and actual speeds,as well as inefficiencies due to manual parameter *** paper uses edge intelligence to propose a novel PID control algorithm that integrates Backpropagation(BP)neural networks to enhance robustness and *** BP neural network is first trained to capture the nonlinear dynamic characteristics of the *** network is then combined with the PID controller to forma hybrid control *** output layer of the neural network directly adjusts the PIDparameters(k_(p),k_(i),k_(d)),optimizing performance for specific driving scenarios through self-learning and weight *** experiments demonstrate that our BP neural network-based PID design significantly outperforms traditional methods,with the response time for acceleration from 0 to 1 m/s improved from 0.25 s to just 0.065 ***,real-world tests on an intelligent vehicle show its ability to make timely adjustments in response to complex road conditions,ensuring consistent speed maintenance and enhancing overall system performance.

关键词： PID control backpropagation neural network hybrid control nonlinear dynamic processes edge intelligence

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An improved Convolutional Neural Network model for human posture detection 42

An improved Convolutional Neural Network model for human pos...

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42nd Chinese Control Conference, CCC 2023

作者： Guo, Xin Liu, Jian Lei, Yaoxu School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300130 China Qinhuangdao Research Institute National Research Center for Rehabilitation Assistants Hebei Qinhuangdao066004 China Hebei University of Technology Control Engineering Technology Innovation Center of Hebei Province Tianjin300401 China

ISBN: (纸本)9789887581543

The classic OpenPose convolutional neural network model has excellent performance in the field of human posture detection. But it also has some shortcomings, such as poor accuracy and low accuracy of human posture detection partly. An improved OpenPose network model is proposed to solve these problems. First of all, the feature extraction network VGG -19 of OpenPose model is replaced by the residual network (ResN et) with residual learning structure to improve the training accuracy of the network. Secondly, at the same stage, the dual-branch parallel structure is changed to the single-branch serial structure to predict the part confidence maps (PCMs) and the part affinity fields (PAFs) of human joint points. Finally, the structure of the convolution kernel is optimized and the residual network is introduced. The accuracy of the algorithm is improved and the computational speed of the model is guaranteed as much as possible. Experimental verification on MPII data sets shows that the detection accuracy of the improved algorithm model in this paper can reach 79.5%, which is 3.9% higher than the original model. At the same time, it has a higher accuracy of detection compared with other human posture detection algorithms. It can be applied to human posture detection with a high pursuit of the accuracy of detection of the model, such as the field of intelligent driving and rehabilitation training and so on. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Neural network models

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Inductive graph unlearning 23

Inductive graph unlearning

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Proceedings of the 32nd USENIX Conference on Security Symposium

作者： Cheng-Long Wang Mengdi Huai Di Wang King Abdullah University of Science and Technology and SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence Iowa State University King Abdullah University of Science and Technology and Computational Bioscience Research Center and SDAIA-KAUST Center of Excellence in Data Science and Artificial Intelligence

ISBN: (纸本)9781939133373

As a way to implement the "right to be forgotten" in machine learning, machine unlearning aims to completely remove the contributions and information of the samples to be deleted from a trained model without affecting the contributions of other samples. Recently, many frameworks for machine unlearning have been proposed, and most of them focus on image and text data. To extend machine unlearning to graph data, GraphEraser has been proposed. However, a critical issue is that GraphEraser is specifically designed for the transductive graph setting, where the graph is static and attributes and edges of test nodes are visible during training. It is unsuitable for the inductive setting, where the graph could be dynamic and the test graph information is invisible in advance. Such inductive capability is essential for production machine learning systems with evolving graphs like social media and transaction networks. To fill this gap, we propose the GUided InDuctivE Graph Unlearning framework (GUIDE). GUIDE consists of three components: guided graph partitioning with fairness and balance, efficient subgraph repair, and similarity-based aggregation. Empirically, we evaluate our method on several inductive benchmarks and evolving transaction graphs. Generally speaking, GUIDE can be efficiently implemented on the inductive graph learning tasks for its low graph partition cost, no matter on computation or structure information. The code is available here: https://***/Happy2Git/GUIDE.

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MMFNet: A Multi-modal and Multi-attention Fusion Network for Multi-task Skin Disease Classification

MMFNet: A Multi-modal and Multi-attention Fusion Network for...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Zhu, Xinlei Qian, Pengjiang Wang, Chuang Liu, Li Gao, Ming Cai, Weiwei Ng, Eddie Yin-Kwee Jiangnan University School of Artificial Intelligence and Computer Science Wuxi China Jiangnan University Engineering Research Center of Intelligent Technology for Healthcare Ministry of Education Wuxi China Affiliated Hospital Jiangnan University Big Data Center Wuxi China Nanyang Technological University School of Mechanical and Aerospace Engineering Nanyang Singapore

ISBN: (纸本)9798350386226

Skin diseases rank among the most prevalent ailments in humans, which underscores the critical importance of early detection and diagnosis. Considering clinical practice, the integration of information from various modalities holds significant potential to enhance diagnostic accuracy and precision. In this study, we propose a multi-modal and multi-attention fusion network for multi-task skin disease classification. We initially devise the Shifted Window Cross-attention Fusion (SWCF) module based on the shifted window self-attention mechanism, leveraging the advantages of the attention mechanism to learn the correlations between different modalities and fully integrate multi-modal features. Subsequently, taking into account the heterogeneity of the data, we employ the Heterogeneous data Cross-attention Fusion (HDCF) module using the idea of information decoupling and separation processing to integrate image and text data. Additionally, we suggest the Dynamic Loss Weight Allocation (DLWA) method for multi-task learning to refine the training procedure. We confirm the superiority of the proposed method on the publicly available multi-modal skin lesion dataset, Derm7pt. The average accuracy of multi-modal skin disease classification is 79.14%, surpassing the current state-of-the-art methods. © 2024 IEEE.

关键词： Multi-task learning

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A Comparative Study of Gaze Estimation Models 25

A Comparative Study of Gaze Estimation Models

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25th International Arab Conference on Information Technology, ACIT 2024

作者： Moubayed, Abdallah Injadat, MohammadNoor Kanan, Mohammad King Fahd University of Petroleum & Minerals Interdisciplinary Research Center for Intelligent Secure Systems Computer Engineering Department Dhahran Saudi Arabia Zarqa University Data Science & Artificial Intelligence Department Zarqa Jordan University of Business and Technology Department of Industrial Engineering Jeddah Saudi Arabia

ISBN: (纸本)9798331540012

The eye-mind hypothesis suggests that people tend to look at what they're actively thinking about, forming the basis of eye and gaze tracking. This concept is gaining attention in deep learning due to its broad applications. The use of AI alongside webcams to monitor eye movements is increasingly popular and expected to grow further. This is further emphasized by recent data showing a growing use of eye gaze estimation techniques, especially in marketing research, e-commerce, and educational tools. Accordingly, multiple previous research works have developed various eye and gaze estimation and tracking models. However, one main limitation is that many models use their own datasets for performance evaluation as well as having different underlying computing resources that are used during training. Consequently, it becomes harder to compare the effectiveness and efficiency of these models. To that end, this work aims at providing a comprehensive comparative study of three well-established eye gaze estimation models, namely OpenGaze, GazeRefineNet, ODABE, and FAZE models using a unified evaluation framework. Experimental results conducted using GazeCapture dataset illustrate that OpenGaze model achieves a mean error of 2.27 cm mean error, GazeRefineNet model achieves 1.91 cm, ODABE model achieves 3.46 cm, and FAZE model achieves 2.9 cm. This indicates that GazeRefineNet outperforms the other models in terms of accuracy while having comparable computational complexity. © 2024 IEEE.

关键词： Market research

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Detection of the Anaerobic Threshold from Cardiopulmonary Exercise Test data with EO-Attention-LSTM 42

Detection of the Anaerobic Threshold from Cardiopulmonary Ex...

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42nd Chinese Control Conference, CCC 2023

作者： Guo, Xin Lun, Zhaoxin Chu, Qitong School of Artificial Intelligence and Data Science Hebei University of Technology Tianjin300130 China Qinhuangdao Research Institute National Research Center for Rehabilitation Technical Aids Qinhuangdao066004 China Control Engineering Technology Innovation Center of Hebei Province Hebei University of Technology Tianjin300401 China

ISBN: (纸本)9789887581543

The physiological indicators at the anaerobic threshold play an essential role in guiding scientific exercise. The gold standard methodology for detecting the anaerobic threshold is expert visual inspection, but its efficiency is low. Given the above problems, we propose an anaerobic threshold detection method combining the equilibrium optimizer (EO) algorithm and Attention-LSTM, which was verified on the data of 334 cardiopulmonary exercise tests. Through comparative experiments, the coefficient of determination (R2) of the Attention-LSTM anaerobic threshold detection model is 0.797, the R2 of the Attention-LSTM model after hyperparameter optimization by EO is 0.834. The results show that the detection error of the anaerobic threshold detection model proposed in this paper is consistent with that of experts, which can provide a certain reference value. © 2023 Technical Committee on Control Theory, Chinese Association of Automation.

关键词： Long short-term memory

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Enhancing Online Reinforcement Learning with Meta-Learned Objective from Offline data

arXiv

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arXiv 2025年

作者： Deng, Shilong Zheng, Zetao He, Hongcai Weng, Paul Shao, Jie University of Electronic Science and Technology of China Chengdu China Sichuan Artificial Intelligence Research Institute Yibin China Data Science Research Center Duke Kunshan University Kunshan China

A major challenge in Reinforcement Learning (RL) is the difficulty of learning an optimal policy from sparse rewards. Prior works enhance online RL with conventional Imitation Learning (IL) via a handcrafted auxiliary objective, at the cost of restricting the RL policy to be sub-optimal when the offline data is generated by a non-expert policy. Instead, to better leverage valuable information in offline data, we develop Generalized Imitation Learning from Demonstration (GILD), which meta-learns an objective that distills knowledge from offline data and instills intrinsic motivation towards the optimal policy. Distinct from prior works that are exclusive to a specific RL algorithm, GILD is a flexible module intended for diverse vanilla off-policy RL algorithms. In addition, GILD introduces no domain-specific hyperparameter and minimal increase in computational cost. In four challenging MuJoCo tasks with sparse rewards, we show that three RL algorithms enhanced with GILD significantly outperform state-of-the-art methods. Copyright © 2025, The Authors. All rights reserved.

关键词： Adversarial machine learning

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