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检索条件"机构=Intelligent Robotics Research Centre Department of Electrical and Computer Systems Engineering"

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Reducing the Computational Cost of Transformers for Person Re-identification

Reducing the Computational Cost of Transformers for Person R...

引用

2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Wang, Wen Lin, Zheyuan Ji, Shanshan Li, Te Gu, Jason Wan, Minhong Zhang, Chunlong Research Center for Intelligent Robotics Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350325706

Transformer-based visual technologies have witnessed remarkable progress in recent years, and person re-identification (ReID) is one of the active research areas that adopts transformers to improve the performance. However, a major challenge of applying transformers to ReID is the high computational cost, which hinders the real-time deployment of such methods. To address this issue, this paper proposes two simple yet effective techniques to reduce the computation of transformers for ReID. The first technique is to eliminate the invalid patches that do not contain any person information, thereby reducing the number of tokens fed into the transformer. Considering that computational complexity is quadratic with respect to input tokens, the second technique partitions the image into multiple windows, applies separate transformers to each window, and merges class tokens from each window, which can reduce the complexity of the self-attention mechanism. By combining these two techniques, our proposed method reduces the SOTA baseline model by 12.2% FLOPs, while slightly improving the rank-1 accuracy and only sacrificing 1.1% mAP on DukeMTMC-ReID dataset. © 2023 IEEE.

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Modeling and Parameter Identification for Human-robot Coupled systems in Powered Lower Limb Prostheses. 5

Modeling and Parameter Identification for Human-robot Couple...

引用

5th World Robot Conference Symposium on Advanced robotics and Automation, WRC SARA 2023

作者： Huang, Yongshan Wang, Xin Liang, Dingkun Xiong, Jiaming Xie, Anhuan Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou311100 China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNS Canada

ISBN: (纸本)9798350307320

Good trajectory tracking results are the basis for the implementation of other upper-level algorithms in powered lower limb prostheses, which rely on dynamics modeling and good parameter identification. However, powered lower limb prosthesis are human-robot coupled systems with human-inthe-loop, and it has been a challenge to model them as well as to perform parameter identification. This paper proposes a modeling method that can realize the decoupling of human-machine, and by constructing the linearized models of human-machine coupled system and prosthetic subsystem about the physical parameter sets respectively, and finally obtaining the relationship of their corresponding physical parameter sets, thus the method of indirectly identifying the parameters of human-machine coupled system through the prosthetic subsystem can be realized. The experimental results verify the feasibility and effectiveness of the method proposed in this paper. And the experimental results also shows that prosthetic controller should take advantage of dynamic of hip motion in order to make the wearer more comfortable and fluid when swinging the leg, and will also make the prosthesis more energy efficient. © 2023 IEEE.

关键词： Energy efficiency

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Integrated Direct/Indirect Adaptive Robust Control for electrical Driven Injection Machine Mold Closing with Accurate Parameter Estimations 16th

Integrated Direct/Indirect Adaptive Robust Control for Elec...

引用

16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhu, Shiqiang Meng, Qiwei Song, Wei Gu, Jason Research Center for Intelligent Robotics Zhejiang Engineering Research Center for Intelligent Robotics Zhejiang Lab Hangzhou Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

In this paper, the control issue of the mold closing system of Injection machine is studied. In actual application, there are two major control problems needed to be addressed in controller design: to ensure the quality of products, the precision of mold needs to be guaranteed;the operation parameters of the machines are willing to be recorded for analysis, such as health monitoring and prognosis. However, the existing controllers usually rely on the servo motor, where the dynamics of the mold closing system is not taken into consideration, that may lead to degraded tracking performances. Moreover, the uncertain model parameters are usually ignored or simply treated as disturbances. Instead in this paper, the nonlinear dynamic model of mold closing system is first proposed including parameter uncertainties and disturbances for controller design. Subsequently, an integrated direct/indirect adaptive robust control law(DIARC) is proposed that not only achieves excellent tracking performance but also attenuates the parameter uncertainties with accurate parameter learning. Comparative experiments are carried out to verify our proposed approach and the results demonstrate the effectiveness of the integrated direct/indirect adaptive robust control. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Robust control

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Decision-Making in Robotic Grasping with Large Language Models 16th

Decision-Making in Robotic Grasping with Large Language Mo...

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16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Liao, Jianfeng Zhang, Haoyang Qian, Haofu Meng, Qiwei Sun, Yinan Sun, Yao Song, Wei Zhu, Shiqiang Gu, Jason Research Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964949

Recent advances in large language models have highlighted their potential to encode massive amounts of semantic knowledge for long-term autonomous decision-making, positioning them as a promising solution for powering the cognitive capabilities of future home-assistant robots. However, while large language models can provide high-level decision, there is still no unified paradigm for integrating them with robots’ perception and low-level action. In this paper, we propose a framework centered around a large language model, integrated with visual perception and motion planning modules, to investigate the robotic grasping task. Unlike traditional methods that only focus on generating stable grasps, our proposed approach can handle personalized user instructions and perform tasks more effectively in home scenarios. Our approach integrates existing state-of-the-art models in a simple and effective way, without requiring any fine-tuning, which makes it low-cost and easy to deploy. Experiments on a physical robot system demonstrate the feasibility of our approach. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Decision making

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Autonomous 3D Reconstruction with Small Unmanned Aerial Vehicle (UAV) using Structure from Motion: Practical Aspects

Autonomous 3D Reconstruction with Small Unmanned Aerial Vehi...

引用

AIAA Science and Technology Forum and Exposition, AIAA SciTech Forum 2025

作者： Rayon, Nathan Stevens, Raymond Sevil, Hakki Erhan Department of Computer Science University of West Florida FL32514 United States Department of Electrical and Computer Engineering University of West Florida FL32514 United States Department of Intelligent Systems & Robotics University of West Florida FL32514 United States

ISBN: (数字)9781624107238

ISBN: (纸本)9781624107238

This paper explores the practical considerations and challenges involved in achieving autonomous 3D reconstruction utilizing small Unmanned Aerial Vehicles (UAVs) through the framework of Structure from Motion (SFM). The utilization of small UAVs equipped with lightweight cameras presents a promising resource for cost-effective and rapid 3D mapping in various domains such as disaster response, infrastructure inspection, and search and rescue. In this study, the critical practical aspects of this process, including height level for taking images, UAV selection, number of images, image acquisition strategies, and computational time. The effectiveness and limitations of autonomous 3D reconstruction with small UAVs through practical insights are summarized, with discussions on potential advancements and areas for future research. This paper serves as a guide for researchers interested in utilizing small UAVs for autonomous 3D reconstruction tasks applications. © 2025, American Institute of Aeronautics and Astronautics Inc, AIAA. All rights reserved.

关键词： Unmanned aerial vehicles (UAV)

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Pre-trained Deep Learning Networks for Advanced Visible Imagery Drone Detection and Recognition 15

Pre-trained Deep Learning Networks for Advanced Visible Imag...

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15th IEEE International Conference on Computational Intelligence and Communication Networks, CICN 2023

作者： Al Dawasari, Hassan J. Bilal, Muhammad Moinuddin, Muhammad Arshad, Kamran Assaleh, Khaled King Abdulaziz University Centre of Excellence in Intelligent Engineering Systems and Electrical and Computer Engineering Department Jeddah Saudi Arabia Ajman University Artificial Intelligence Research Centre Ajman United Arab Emirates

ISBN: (纸本)9798350324433

In this paper, we introduce a state-of-the-art deep learning technique designed to accurately differentiate between drones and birds. This technique is particularly effective in reducing hazards associated with unauthorized drones, especially in airport environments where such drones can cause significant flight disruptions. Our approach involves the utilization of a meticulously compiled image dataset for testing, yielding results that surpass previous detection methods outlined in existing literature. Among the models evaluated, ResNet18 emerges as a standout, achieving an impressive average precision (AP) of 0.739 in medium area ratios. A key feature of our method is its ability not only to detect drones but also to precisely distinguish them from birds. The dataset employed in this research is derived from the publicly accessible real-world data of the 2020 Drone vs. Bird Detection Challenge. © 2023 IEEE.

关键词： Drones

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Vision-Based Categorical Object Pose Estimation and Manipulation 16th

Vision-Based Categorical Object Pose Estimation and Manipul...

引用

16th International Conference on intelligent robotics and Applications, ICIRA 2023

作者： Meng, Qiwei Liao, Jianfeng Jun, Shao Xu, Nuo Xu, Zeming Sun, Yinan Sun, Yao Zhu, Shiqiang Gu, Jason Song, Wei Center for Intelligent Robotics Zhejiang Lab Hangzhou China Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou China School of Mechanical Engineering Zhejiang University Hangzhou China Department of Electrical and Computer Engineering Dalhousie University Halifax Canada

ISBN: (纸本)9789819964826

Object manipulation and environment interaction are of great significance for intelligent robots, especially service robots working under unstructured household and office scenarios. This paper proposes a novel approach for categorical unseen object grasping and manipulation. Different from recently popular end-to-end reinforcement learning methods, we develop models for geometric primitive abstraction of target objects, and accordingly estimate their pose as well as generate task-orientated grasp points. Such design emphasizes visual perception in guiding robotic manipulation, thereby enhancing model interpretability and reliability during implementation. In addition, we also conduct object grasping experiments both under simulation and real-world settings, which further verify the effectiveness and superiority of our method. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： intelligent robots

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FAIR-FATE: Fair Federated Learning with Momentum 23rd

FAIR-FATE: Fair Federated Learning with Momentum

引用

23rd International Conference on Computational Science, ICCS 2023

作者： Salazar, Teresa Fernandes, Miguel Araújo, Helder Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering of the University of Coimbra University of Coimbra Coimbra Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering of the University of Coimbra University of Coimbra Coimbra Portugal

ISBN: (纸本)9783031359941

While fairness-aware machine learning algorithms have been receiving increasing attention, the focus has been on centralized machine learning, leaving decentralized methods underexplored. Federated Learning is a decentralized form of machine learning where clients train local models with a server aggregating them to obtain a shared global model. Data heterogeneity amongst clients is a common characteristic of Federated Learning, which may induce or exacerbate discrimination of unprivileged groups defined by sensitive attributes such as race or gender. In this work we propose FAIR-FATE: a novel FAIR FederATEd Learning algorithm that aims to achieve group fairness while maintaining high utility via a fairness-aware aggregation method that computes the global model by taking into account the fairness of the clients. To achieve that, the global model update is computed by estimating a fair model update using a Momentum term that helps to overcome the oscillations of non-fair gradients. To the best of our knowledge, this is the first approach in machine learning that aims to achieve fairness using a fair Momentum estimate. Experimental results on real-world datasets demonstrate that FAIR-FATE outperforms state-of-the-art fair Federated Learning algorithms under different levels of data heterogeneity. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Momentum

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Real-time Architecture for Audio-Visual Active Speaker Detection

Real-time Architecture for Audio-Visual Active Speaker Detec...

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Huang, Min Wang, Wen Lin, Zheyuan Tesema, Fiseha B. Ji, Shanshan Gu, Jason Wan, Minhong Song, Wei Li, Te Zhu, Shiqiang The Intelligent Robotics Research Center Zhejiang Lab Zhejiang Engineering Research Center for Intelligent Robotics Hangzhou311100 China Department of Electrical and Computer Engineering Dalhousie University Canada

ISBN: (纸本)9781665481090

Continuously measuring the speaking state of users with robot in a human-robot Interaction(HRI) system improves metrics of interaction quality. Meanwhile, mainstream active speaker detection (ASD) algorithms emphasize achieving high AUCs at frame level in the AVA-Active Speaker dataset and pay less attention to get real-time performance in robotic systems. In this paper, we propose a model named FSDNet to keep a high AUC score in the AVA-Active Speaker dataset while reducing time cost, our model increase AUC score by 0.1% compared with the State-Of-The-Art and need only 75% running time. Furthermore, we put forward an architecture with a time-related prediction function to make our algorithm more effective and generative in interactive robotic systems. The code is released at https://***/huangmin9966/FSDNet-RealTimeArch. © 2022 IEEE.

关键词： Human robot interaction

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Quantum Calculus-Based Two-Dimensional Least Mean Square Algorithm 58

Quantum Calculus-Based Two-Dimensional Least Mean Square Alg...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Moinuddin, Muhammad Zerguine, Azzedine Al-Saggaf, Ubaid M. Arif, Muhammad King Abdulaziz University Electrical and Computer Engineering Department The Centre of Excellence in Intelligent Engineering Systems Jeddah Saudi Arabia Kfupm Electrical Engineering Department The Interdisciplinary Research Center for Communication Systems and Sensing Dhahran31261 Saudi Arabia Institute of Networked and Embedded Systems University of Klagenfurt Klagenfurt Austria The Ubiquitous Sensing Systems Lab University of Klagenfurt-Silicon Austria Labs Klagenfurt Austria

ISBN: (纸本)9798350354058

Recent work of the one-dimensional q-LMS algorithm give insight that the Quantum Calculus based gradient called the q-gradient is capable of improving the convergence behavior in contrast to the standard LMS algorithm. The reason for this is the fact that the q-derivative takes larger steps in minimizing a cost function as it evaluates the secant in contrast to the tangent of the cost function. Motivated by this, we propose a two-dimensional version of the q-LMS algorithm. More precisely, we employ Quantum Calculus based q-gradient in steepest descent optimization of mean-square-error (MSE) cost function in 2D adaptive filtering scenario to obtain two-dimensional q-LMS (2D q-LMS) algorithm. A thorough analytical investigation of the proposed 2D q-LMS algorithm in both mean and mean-square sense is provided. Both the transient and steady-state convergence behaviours are examined. Consequently, excess MSE (EMSE) learning curve and its steady-state expression are evaluated in closed form. Simulation results are presented for the application of noise cancelation in images to show the superiority of the proposed 2D q-LMS algorithm. © 2024 IEEE.

关键词： Cost functions

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