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2023 IEEE International Conference on Real-Time Computing and robotics, RCAR 2023

作者： Jiang, Jialin Yang, Lidong Zhang, Li The Chinese University of HongKong Department of Mechanical and Automation Engineering Hong Kong Hong Kong Department of Industrial and Systems Engineering China The Chinese University of Hong Kong Department of Surgery Hong Kong The Chinese University of Hong Kong Cuhk T Stone Robotics Institute Hong Kong Hong Kong The Chinese University of Hong Kong Chow Yuk Ho Technology Center for Innovative Medicine Hong Kong Hong Kong Science Park Multi-Scale Medical Robotics Center China

ISBN: (纸本)9798350327182

Untethered microrobots possess a promising perspective for micro-manipulation applications. With specifically designed morphologies and structures, microrobots are able to perform controllable delivery of target objects. However, the manipulation process still lacks autonomy, the picking, transporting, and releasing behaviors of microrobots need analysis. In this paper, we propose to achieve automated microrobotic manipulation with multimodal magnetic microswarms. The microswarm is composed of numerous Fe3O4 nanoparticles. When exposed to different dynamic magnetic fields (rotating magnetic field and oscillating magnetic field), the swarm could exhibit corresponding forms and generate attracting and repelling effects for micro-objects, respectively. Exploiting these two types of swarms, precise and controllable picking and releasing can be realized. The manipulation process is quantitatively divided into five stages, and a finite state machine (FSM) is presented to govern the transition between the stages. To guarantee accurate motion control of the swarm, a disturbance observer (DOB)-based super-twisting sliding mode control (STSMC) is designed. Experiments are conducted to validate the feasibility of the proposed manipulation scheme. The results demonstrate that the swarm is capable of automatically and effectively delivering micro-cargoes with different sizes to the target positions. © 2023 IEEE.

关键词： Magnetite

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Detection and Classification in Ballistic Drone Capturing Based on Transfer Learning Networks Using Infrared Images 6

Detection and Classification in Ballistic Drone Capturing Ba...

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6th IEEE Conference on Information and Communication Technology, CICT 2022

作者： Harsha Vardhan Reddy, M. Gothandaraman, Rajkumar Muthuswamy, Sreekumar Amrita School of Engineering Amrita Vishwa Vidyapeetham Department of Mechanical Engineering Chennai India Indian Institute of Information Technology Design and Manufacturing Center for Al IoT and Robotics Department of Mechanical Engineering Chennai Kancheepuram India

ISBN: (纸本)9781665473125

Unmanned aerial vehicles and drones are flying objects carrying small payloads for various applications, including agriculture, photography for weddings & religious ceremonies, e-commerce deliveries, remote sensing, 3D mapping, surveillance and defense. These flying objects also called as drones are available in two variants;1. Fixed wing propeller type, and 2. Multi-wings propeller type. Nowadays, UAVs and Drones are the primary threat to the national security on the mainland and the line of actual control. Generally, these threat-rising drones can be eradicated by anti-missile techniques, which are not economical however will destroy them. The objective of the work here is to design and develop a ballistic drone capturing system comprising two main steps;1. classification of UAVs from the flying birds and 2. Capturing the UAVs without causing significant damage to the drone. Here, the methodology uses computer-generated infrared images of the drone in the pretrained network architecture, which acts as the Convolution Neural Network (CNN) for classifying drones and birds. The performance of AlexNet and ResNet-50 performs better and it shows 100 percent efficiency and small distribution with more stability. In the near future, the ballistic capturing system will be designed and developed as a product, which would support acquiring data about malicious drones. © 2022 IEEE.

关键词： Drones

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Human-Like Reverse Parking using Deep Reinforcement Learning with Attention Mechanism

Human-Like Reverse Parking using Deep Reinforcement Learning...

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IEEE Symposium on Intelligent Vehicle

作者： Zhuo Qiu Shitao Chen Jiamin Shi Fei Wang Nanning Zheng National Engineering Research Center for Visual Information and Applications and Institute of Artificial Intelligence and Robotics National Key Laboratory of Human-Machine Hybrid Augmented Intelligence Xi’an Jiaotong University Xi’an P.R. China

ISBN: (数字)9798350348811

ISBN: (纸本)9798350348828

This study explores efficient and safe Automated Valet Parking (AVP) strategies in unstructured and dynamic environments. Existing approaches utilizing reinforcement learning neglected the interaction between dynamic agents and ego vehicle, and disregarded human driving patterns, leading to their ineffectiveness in unstructured dynamic environments. We propose a novel hybrid attention mechanism that comprehends the mixed interactions between static and dynamic elements, aiding autonomous vehicles in advanced planning. We implemented a guidance system based on human preferences, eliminating the need for expert data and expediting the training process via intermediate planning stages, thereby facilitating parking maneuvers akin to human drivers. The model was trained and validated in a range of parking situations. The experimental outcomes indicate that our method possesses robust adaptability and navigation skills in static and dynamic environments.

关键词： Training Attention mechanisms Trajectory planning Navigation Heuristic algorithms Feature extraction Deep reinforcement learning

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Terahertz Communications and Sensing Overview 3

Terahertz Communications and Sensing Overview

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3rd International Conference on Advancement in Electronics and Communication engineering, AECE 2023

作者： Salama, Ramiz Altrjman, Chadi Al-Turjman, Fadi Yadav, Satya Prakash Vats, Satvik Gupta, Richa Ai and Robotics Institute Research Center for Ai and IoT Near East University Department of Computer Engineering Near East Boulevard TRNC Mersin 10 Nicosia99138 Turkey Waterloo University Department of Chemical Engineering ONN2L 3G1 Canada Ai and Robotics Institute Near East University Artificial Intelligence Engineering Dept Mersin10 Nicosia Turkey Department of Computer Science and Engineering Greater Noida201306 India Graphic Era Hill University Department of Computer Science and Engineering Dehradun India Raj Kumar Goel Institute of Technology Department of Electronics & Communication Engineering India

ISBN: (纸本)9798350330724

Terahertz (THz) (0.1-10 THz) wireless communication is one of the cornerstones of the next 6G wireless networks. THz frequencies have the ability to dramatically increase wireless capacity performance and enable high-resolution environment sensing if applied properly due to the enormous quantity of accessible bandwidth. However, the usage of wireless devices in high-frequency bands like THz is constrained by a very unpredictable and dynamic channel. The ultimate result is essentially unreliable intermittent connections since these channel constraints have a naturally restricted communication range and a high susceptibility to blocking and chemical absorption. Because of this, the THz band's potential for high-rate communications and high-resolution sensing may be hindered. This study thoroughly examines the steps necessary to build up and operate next-generation THz wireless networks that will work together to deliver a variety of communication and sensing services in this environment. We first lay the groundwork for this by defining the THz frequency range's fundamentals. Using these fundamentals as a foundation, we outline and carefully investigate seven specific qualities that characterize THz wireless systems: Some of the subjects discussed include the quasi-opticality of the band, wireless architectures suited for THz, synergy with lower frequency bands, cooperative sensing and communication systems, PHY-layer protocols, spectrum access techniques, and real-time network optimization. These seven distinctive features enable our understanding of how to re-engineer wireless systems as we know them today to fit THz bands and their specific settings. On the one hand, THz systems make use of its quasi-optimality and may turn any sensing opportunity into a communication problem, aiding in the development of a new breed of flexible wireless systems that can do many jobs beyond straightforward communications. THz systems can alternatively use intelligent surfaces, lower

关键词： Internet of Everything

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Estimation of Transmitter Inductance on Foreign Object in Wireless Power Transfer

Estimation of Transmitter Inductance on Foreign Object in Wi...

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International Conference on Electrical Machines and Systems (ICEMS)

作者： Min Seung Song Seung Ahn Chae Gwan Soo Park Department of Electrical and Electronic Engineering Pusan National University Busan South Korea Robotics Institute of Non-Destructive Inspection Pusan National University Busan South Korea

ISBN: (数字)9784886864406

ISBN: (纸本)9798350379105

Foreign objects with magnetic or conductive properties between the transmitter and receiver coils alter the resonant frequency, reducing output and efficiency. While circuit parameter-based detection methods offer the simplest approach for detecting foreign objects, considering cost and installation space, most parameters are affected by load characteristics, transmission distance, and coil alignment. Unlike other parameters, self-inductance is independent of the load resistance and coupling coefficient, making it the most suitable parameter for circuit parameter-based detection methods. However, due to the challenges of measuring self-inductance during power transmission, it must be estimated using measurable parameters. In this paper, we propose a method to estimate the transmitter inductance by expressing it solely in terms of measurable parameters based on the circuit model analysis.

关键词： Coils Resistance Inductance Power measurement Estimation Power transmission Finite element analysis Electrical resistance measurement Inductance measurement Load modeling

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Competitive Equilibrium in Microgrids with Dynamic Loads

Competitive Equilibrium in Microgrids with Dynamic Loads

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American Control Conference (ACC)

作者： Zeinab Salehi Yijun Chen Ian R. Petersen Elizabeth L. Ratnam Guodong Shi Research School of Engineering The Australian National University Canberra Australia Australian Center for Field Robotics School of Aerospace Mechanical and Mechatronic Engineering The University of Sydney NSW Australia

ISBN: (数字)9798350382655

ISBN: (纸本)9798350382662

In this paper, we consider microgrids that interconnect prosumers with distributed energy resources and dynamic loads. Prosumers are connected through the micro- grid to trade energy and gain profit while respecting the network constraints. We establish a local energy market by defining a competitive equilibrium which balances energy and satisfies voltage constraints within the microgrid for all time. Using duality theory, we prove that under some convexity assumptions, a competitive equilibrium is equivalent to a social welfare maximization solution. Additionally, we show that a competitive equilibrium is equivalent to a Nash equilibrium of a standard game. In general, the energy price for each prosumer is different, leading to the concept of locational prices. We investigate a case under which all prosumers have the same locational prices. Additionally, we show that under some assumptions on the resource supply and network topology, locational prices decay to zero after a period of time, implying the available supply will be more than the demand required to stabilize the system. Finally, two numerical examples are provided to validate the results, one of which is a direct application of our results on electric vehicle charging control.

关键词： Network topology Microgrids Games Dynamic scheduling Nash equilibrium Electric vehicle charging Nonlinear dynamical systems

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Fuzzy control system design for wheel slip prevention and tracking of desired speed profile in electric trains

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Asian Journal of Control 2022年第1期24卷 388-400页

作者： Moaveni, Bijan Rashidi Fathabadi, Fatemeh Molavi, Ali Center of excellence for Modelling and Control of Complex Systems Faculty of Electrical Engineering K. N. Toosi University of Technology Tehran Iran Department of Electrical and Computer Engineering Western Michigan University MI United States Institute of Robotics and Industrial Informatics Polytechnical University of Catalonia Barcelona Spain

This study proposes an anti-slip control system for electric trains based on the fuzzy logic theory, which prevents the wheels from slipping during the acceleration and simultaneously tracks the desired speed profile. To improve the control performance, the train longitudinal velocity and the slip ratio are estimated. By using a field-oriented control (FOC), the angular speed of the traction motor is controlled. The fuzzy control system determines the desired angular speed of the traction motor as the reference input of FOC to obtain the desired slip ratio and track the desired speed profile. Simulation results show the effectiveness of the control system in various wheel–rail surface conditions based on the real parameters of ER24PC locomotive. © 2020 Chinese Automatic Control Society and John Wiley & Sons Australia, Ltd

关键词： Traction motors

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Multi-Frequency-Aware Patch Adversarial Learning for Neural Point Cloud Rendering

Multi-Frequency-Aware Patch Adversarial Learning for Neural ...

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IEEE/ASME (AIM) International Conference on Advanced Intelligent Mechatronics

作者： Jay Karhade Haiyue Zhu Ka-Shing Chung Rajesh Kumar Tripathy Wei Lin Marcelo H. Ang Robotics Institute Carnegie Mellon University Pittsburgh US Singapore Institute of Manufacturing Technology (SIMTech) Agency for Science Technology and Research (A*STAR) Singapore Advanced Robotics Centre College of Design and Engineering National University of Singapore Singapore Birla Institute of Technology and Science Pilani Hyderabad India

ISBN: (数字)9798350355369

ISBN: (纸本)9798350355376

We present a neural point cloud rendering pipeline through a novel multi-frequency-aware patch adversarial learning framework. The proposed approach aims to improve the rendering realness by minimizing the spectrum discrepancy between real and synthesized images, especially on the high-frequency localized sharpness information which causes image blur visually. Specifically, a patch multi-discriminator scheme is proposed for the adversarial learning, which combines both spectral domain (Fourier Transform and Discrete Wavelet Transform)discriminators as well as the spatial (RGB) domain discriminator to force the generator to capture global and local spectral distributions of the real images. The proposed multi-discriminator scheme not only helps to improve rendering realness, but also enhance the convergence speed and stability of adversarial learning. Moreover, we introduce a noise-resistant voxelisation approach by utilizing both the appearance distance and spatial distance to exclude the spatial outlier points caused by depth noise. Our entire architecture is fully differentiable and can be learned in an end-to-end fashion. Extensive experiments show that our method produces state-of-the-art results for neural point cloud rendering by a significant margin.

关键词： Point cloud compression Solid modeling Fourier transforms Wavelet domain Three-dimensional displays Rendering (computer graphics) Adversarial machine learning

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A Fully Test-Time Adaptation Method for Motor Imagery

SSRN

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SSRN 2024年

作者： Peng, Yiheng Luo, Jingjing Wang, Hongbo Chen, Li Guo, Shijie Guo, Yuzhu Li, Yang The Institute of AI and Robotics Academy for Engineering and Technology Fudan University Shanghai200433 China Engineering Research Center of AI and Robotics Ministry of Education Shanghai200043 China Department of Automation Sciences and Electrical Engineering Beihang University Beijing100191 China School of Automation Science and Electrical Engineering China

Transfer learning offers a promising solution to the annotation and calibration challenges associated with motor imagery (MI) electroencephalogram (EEG) decoding during neural-feedback rehabilitation. In this study, we address the challenge of online unsupervised cross-subject adaptation in MI-EEG classification, where target samples arrive sequentially, and we should adapt the model online with limited target data. We focus on the testing stage and propose a MI Fully Test-Time Adaptation (MI-FTTA) method that essentially enables online training using limited and unlabeled target data. MI-FTTA effectively alleviates the influence of noisy pseudo labels with teacher-student mutual learning and time-constrained sample selection. Through recalculating normalization statistics with limited accessible source and target information and prototype-based cross-domain contrastive learning, MI-FTTA adaptively enhances the representational capability of models. It thoroughly harnesses the limited online target samples to enhance adaptation performance. Experimental evaluations are conducted using three classification datasets , including two-class (BCI-IV2b), three-class (High-Gamma), and four-class (BCI-IV2a) scenarios. MI-FTTA demonstrates its superiority by surpassing direct source model deployment with an average performance gain of 4.11\% across different scenarios. Notably, our proposed approach also achieves a significant improvement over 12 different online test-time adaptation algorithms at three different backbones, thus affirming its *** work represents early endeavor in source-free fully test-time adaptation for cross-subject MI-EEG decoding. © 2024, The Authors. All rights reserved.

关键词： Transfer learning

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Diffusion Model with Cross Attention as an Inductive Bias for Disentanglement

arXiv

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arXiv 2024年

作者： Yang, Tao Lan, Cuiling Lu, Yan Zheng, Nanning National Key Laboratory of Human-Machine Hybrid Augmented Intelligence National Engineering Research Center for Visual Information and Applications Institute of Artificial Intelligence and Robotics Xi'an Jiaotong University China Microsoft Research Asia China

Disentangled representation learning strives to extract the intrinsic factors within observed data. Factorizing these representations in an unsupervised manner is notably challenging and usually requires tailored loss functions or specific structural designs. In this paper, we introduce a new perspective and framework, demonstrating that diffusion models with cross-attention itself can serve as a powerful inductive bias to facilitate the learning of disentangled representations. We propose to encode an image into a set of concept tokens and treat them as the condition of the latent diffusion model for image reconstruction, where cross-attention over the concept tokens is used to bridge the encoder and U-Net of diffusion model. We analyze that the diffusion process inherently possesses the time-varying information bottlenecks. Such an information bottlenecks and cross-attention act as strong inductive biases for promoting disentanglement. Without any regularization term in loss function, this framework achieves superior disentanglement performance on the benchmark datasets, surpassing all previous methods with intricate designs. We have conducted comprehensive ablation studies and visualization analyses, shedding a light on the functioning of this model. We anticipate that our findings will inspire more investigation on exploring diffusion model for disentangled representation learning towards more sophisticated data analysis and understanding. Copyright © 2024, The Authors. All rights reserved.

关键词： Structural design

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