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arXiv 2024年

作者： Zhilin, Igor Vinogradov, Evgenii Autonomous Robotics Research Center Technology Innovation Institute United Arab Emirates Department of Electrical Engineering KU Leuven Belgium

To keep supporting 6G requirements, the radio access infrastructure will increasingly densify. Cell-free (CF) networks offer extreme flexibility by coherently serving users with multiple Access points (APs). This paradigm requires precise and stable phase synchronization. In this article, we adapt the standardized 5G NR setup (subcarrier spacing, OFDM symbol duration and allocation) to investigate the effect of Phase Noise (PN) on the simulated performance of scalable CF networks. In contrast to the prior literature relying on the simplified model of a free-running oscillator with the Wiener process, we deploy a realistic hardware-inspired phase noise model reproducing the Local Oscillator (LO) phase drift. Our results demonstrate that even affordable LOs offer sufficient stability to ensure negligible loss of uplink Spectral Efficiency (SE) on the time scale of the standardized 5G Transmission Time Interval of 1 ms. This study substantiates the feasibility of CF networks based on 5G standards. © 2024, CC BY-NC-SA.

关键词： Phase noise

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UAV Forest Fire Detection based on RepVGG-YOLOv5

UAV Forest Fire Detection based on RepVGG-YOLOv5

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2022 IEEE International Conference on robotics and Biomimetics, ROBIO 2022

作者： Song, Kang Zhang, Yueyuan Lu, Bo Chi, Wenzheng Sun, Lining Department of Automation Engineering School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Ningbo Institute of Materi-als Technology & Engineering Chinese Academy of Sciences Zhejiang Key Laboratory of Robotics and Intelligent Manufacturing Equipment Technology Ningbo315201 China Soochow University Robotics and Microsystems Center School of Mechanical and Electrical Engineering Suzhou215137 China

ISBN: (纸本)9781665481090

The safety of forest resources is of great im-portance to natural and public safety, and the efficient and accurate detection of forest fires is an issue of close concern. Considering the limitations of traditional forest fire detection methods, such as the limited range of view from watchtowers and insufficient resolution of satellite images, a deep learning-based Unmanned Aerial Vehicle (UAV) fire detection system has been proposed. With its high mobility, images of forest fires can be captured by camera-equipped UAV and transmitted to the ground station in real time. The ground station uses a deep learning object detection algorithm to achieve the recognition of fire. In this paper, we choose the state-of-the-art object detection algorithm YOLOv5 for the UAV forest fire detection scenario, and to further improve the detection performance of YOLOv5, we propose RepVGG-YOLOv5 by drawing on the RepVGG network, a training-time multi-branch topology with an inference-time plain architecture. Then we test the models on a self-built dataset, and the experimental results show that the improved RepVGG-YOLOv5 model outperforms the YOLOv5 model in terms of detection performance and detection speed, which proves the feasibility of this paper's model in the UAV forest fire detection systems and the effectiveness of the model improvement. Furthermore, the great potential of this paper's object detection model for real-time applications of accurate forest fire detection has also been demonstrated. © 2022 IEEE.

关键词： Object detection

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Human-Humanoid Robots Cross-Embodiment Behavior-Skill Transfer Using Decomposed Adversarial Learning from Demonstration

arXiv

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arXiv 2024年

作者： Liu, Junjia Li, Zhuo Yu, Minghao Dong, Zhipeng Calinon, Sylvain Caldwell, Darwin Chen, Fei Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Idiap Research Institute Martigny Switzerland Department of Advanced Robotics Istituto Italiano di Tecnologia Genoa Italy

Humanoid robots are envisioned as embodied intelligent agents capable of performing a wide range of human-level loco-manipulation tasks, particularly in scenarios requiring strenuous and repetitive labor. However, learning these skills is challenging due to the high degrees of freedom of humanoid robots, and collecting sufficient training data for humanoid is a laborious process. Given the rapid introduction of new humanoid platforms, a cross-embodiment framework that allows generalizable skill transfer is becoming increasingly critical. To address this, we propose a transferable framework that reduces the data bottleneck by using a unified digital human model as a common prototype and bypassing the need for re-training on every new robot platform. The model learns behavior primitives from human demonstrations through adversarial imitation, and the complex robot structures are decomposed into functional components, each trained independently and dynamically coordinated. Task generalization is achieved through a human-object interaction graph, and skills are transferred to different robots via embodimentspecific kinematic motion retargeting and dynamic fine-tuning. Our framework is validated on five humanoid robots with diverse configurations, demonstrating stable loco-manipulation and highlighting its effectiveness in reducing data requirements and increasing the efficiency of skill transfer across platforms. Copyright © 2024, The Authors. All rights reserved.

关键词： Anthropomorphic robots

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Towards Robo-Coach: Robot Interactive Stiffness/Position Adaptation for Human Strength and Conditioning Training

Towards Robo-Coach: Robot Interactive Stiffness/Position Ada...

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IEEE International Conference on robotics and automation (ICRA)

作者： Chenzui Li Xi Wu Tao Teng Sylvain Calinon Fei Chen Department of Mechanical and Automation Engineering T-Stone Robotics Institute The Chinese University of Hong Kong Hong Kong Hong Kong Center for Logistics Robotics Hong Kong Idiap Research Institute Martigny Switzerland

ISBN: (数字)9798350384574

ISBN: (纸本)9798350384581

Traditional strength and conditioning training relies on the utilization of free weights, such as weighted implements, to elicit external stimuli. However, this approach poses a significant challenge when attempting to modify or adjust the loads within a single training set. This paper introduces an innovative method for achieving adjustable loads during resistance training by leveraging physical Human-Robot Interaction (pHRI). The primary objective is to regulate targeted muscle activation through the use of Robo-Coach (robotic coach system). We first utilize a Task-Parameterized Gaussian Mixture Model (TP-GMM) to learn the motion of coach demonstration, which can be generalized for the trainees. The 3D path extracted from the generated trajectory is then projected onto a 2D plane with respect to the direction of the load. Furthermore, we propose a hybrid stiffness/position generator for online task execution. This generator determines the desired positions in the 2D plane according to the contact point displacements in the stimuli direction and, simultaneously, sets the desired stiffness based on the muscle activation feedback. Finally, the Robo-Coach is implemented with a variable impedance controller to achieve load-adjustable resistance training with the trainee. The biceps curl exercises were conducted and the results showed favorable performance, indicating the effectiveness of this approach.

关键词： Training Three-dimensional displays Human-robot interaction Muscles Generators Trajectory Impedance

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Robust Model Predictive Control for Onboard Interleaved Multilevel Boost DC-DC Converter in DC Shipboard Microgrids

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IEEE Transactions on Transportation Electrification 2025年

作者： Li, Zhan Chen, Junyu Li, Kerui Xu, Zhao Liu, Yonglu Wang, Minghao The Hong Kong Polytechnic University Department of Electrical and Electronic Engineering Hong Kong Hong Kong The City University of Hong Kong Department of Electrical Engineering Hong Kong Hong Kong Shenzhen Research Institute Department of Electrical and Electronic Engineering China The Hong Kong Polytechnic University Research Institute of Smart Energy Hong Kong Hong Kong Central South University School of Automation Changsha China The Department of Electrical and Computer Engineering China University ofMacau Advanced Research Institute in Hengqin Zhuhai China

DC shipboard microgrids (SMGs) are a low-carbon and feasible solution to host various distributed energy resources (DERs) and shipboard loads. However, the intermittency of DERs and constant power load (CPL) characteristic of the shipboard loads will compromise the stability of DC SMGs. To accommodate DERs and address the stability issue in space-stringent DC SMGs simultaneously, a compound large-signal stabilization scheme is proposed in this paper to operate the interleaved multilevel boost DC-DC converter (IMBDC) in DC SMGs. It combines the high-order nonlinear disturbance observer (NDO) technique and the offset-free model predictive control (MPC) algorithm. Specifically, the uncertain variations of CPLs and circuit parameters are modeled as disturbances. High-order NDOs are used to estimate these disturbances accurately within a finite time. Depending on the estimation and system model, the MPC will regulate the system states against large-signal disturbances. Simulation results demonstrate that the proposed algorithm outperforms the traditional linear and nonlinear control schemes with better stability and dynamic performances. Both the real-time simulation and hardware experiment results are provided to further validate the applicability and effectiveness of the proposed control strategy. © 2015 IEEE.

关键词： Predictive control systems

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Intelligent Accident Recognition and Notification System using Hybrid Deep Learning and IoT 4

Intelligent Accident Recognition and Notification System usi...

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4th International Conference on Soft Computing for Security Applications, ICSCSA 2024

作者： Sathish Kumar, P. Singh Rawat, Ramesh Shrimali, Manish Parikh, Swapnil Patil, Harshal Maranan, Ramya United Institute of Technology Department of Robotics & Automation Tamil Nadu Coimbatore641020 India Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248 002 India Department of Computer Science and IT Rajasthan Udaipur313 003 India Parul Institute of Engineering and Technology Parul University Department of Computer Science and Engineering Gujarat 391760 India Symbiosis Institute of Technology Symbiosis International Deemed University Computer Science and Engineering Maharashtra Pune412 115 India Saveetha School of Engineering SIMATS Department of Research and Innovation Tamil Nadu Chennai602105 India

ISBN: (纸本)9798331515720

Ensuring the safety of individuals should be a top priority. Everyone wants to return home safely. Many riders continue to disregard safety constraints, increasing the chance of accidents that result in death. It is critical to incorporate the latest technology into road monitoring systems to make cities smarter and to ensure rider safety. For this research, Deep Learning (DL) and the Internet of Things (IoT) were chosen for effective road surveillance and alert systems. The primary goal of the study is to detect and inform riders about accidents that occur along their path and to provide details about the location of the detected accident. Additionally, the system provides the vehicle's speed. To identify automobile accidents in the footage, a hybrid DL model that includes Convolutional Neural Network-Bidirectional Long Short-Term Memory (CNN-Bi-LSTM) is suggested. For the model's training, both accident and non-Accident data were collected. The model is evaluated using three metrics: Accuracy, loss, and execution time. The next step is to upload the recommended model to the ThingSpeak cloud. A Graphical User Interface (GUI) is being created in the ThingSpeak cloud. The GUI displays the vehicle's speed, accident images, and the precise location of the accident scene using the Raspberry Pi, speed sensor, Pi-camera, and Global Positioning System (GPS) module. An LED is also integrated into the GUI to alert the user. A real-world test is conducted on the designed system. The proposed solution has many advantages and will ensure driver safety. © 2024 IEEE.

关键词： Graphical user interfaces

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Heavy Overload Transformer Oil Temperature Prediction Method Based on Multivariate Spatio-Temporal Convolutional Network 6

Heavy Overload Transformer Oil Temperature Prediction Method...

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6th International Conference on Communications, Information System and Computer engineering, CISCE 2024

作者： Qin, Jinfei Li, Binbin Luo, Sha Huang, Weimin Lu, Yixiang Lyu, Junwen State Grid Anhui Electric Power Co. Ltd. Power Science Research Institute China Anhui Electric Power Company Limited The Equipment Department of State Grid China College of Electrical Engineering and Automation Anhui University Hefei China

ISBN: (纸本)9798350353174

The top layer oil temperature of a transformer under heavy overload conditions can change rapidly, which affects the insulation performance of the transformer and shortens its operational life. Therefore, the prediction of top layer oil temperature can provide an important basis for early warning of transformer failure. However, single oil temperature data cannot accurately reflect the oil temperature change rule, and the multivariate oil temperature spatio-temporal features are difficult to capture. In order to solve the existing problems, this paper proposes a new heavy overload transformer oil temperature prediction method based on multivariate spatio-temporal convolutional network (MSTCN). The top oil temperature, environmental temperature and load data are first normalized. Then the dataset is inputted into MSTCN to explore the temporal information through gated recurrent unit, utilized the dimensionality-enhanced channel attention mechanism to focus on more important variables and detailed features, and took the convolutional and self-attention mechanism to mine the spatial information. Finally, the prediction results are output through fully connected layers and denormalization. The method is compared with the advanced techniques, and the effectiveness and advancement of the method is proved through experiments. © 2024 IEEE.

关键词： Oil filled transformers

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Convergent effects of peptides on the initiation of feeding motor programs in the mollusk Aplysia

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Journal of Neurophysiology 2025年第5期133卷 1368-1379页

作者： Evans, Colin G. Barry, Michael A. Reaver, Carrie N. Patel, Paras R. Chestek, Cynthia A. Perkins, Matthew H. Jing, Jian Cropper, Elizabeth C. Department of Neuroscience Friedman Brain Institute Icahn School of Medicine at Mount Sinai New York NY United States Department of Biomedical Engineering Biointerfaces Institute University of Michigan Ann Arbor MI United States Department of Electrical Engineering and Computer Science Neurosciences Program Robotics Program University of Michigan Ann Arbor MI United States State Key Laboratory of Pharmaceutical Biotechnology Institute for Brain Sciences Chemistry and Biomedicine Innovation Center Jiangsu Engineering Research Center for MicroRNA Biology and Biotechnology Advanced Institute for Life Sciences School of Life Sciences Nanjing University Nanjing China

Neuropeptides configure the feeding network of Aplysia. For example, egestive activity is promoted by small cardioactive peptide (SCP), and ingestive activity is promoted by a combination of feeding circuit activating peptide (FCAP) and cerebral peptide 2 (CP-2). In addition, SCP and FCAP/CP-2 have a common network effect that does not contribute to motor program specification. They increase the excitability of an interneuron, B63. In this report, we further characterized the effects of peptides on B63. We performed voltage-clamp experiments and used a step protocol to look at steady-state currents. We found that SCP and FCAP/CP-2 both induced an inward current that was virtually absent in low-sodium saline. Previous work has established that B63 is unusual in the feeding circuit in that subthreshold depolarizations are autonomously generated that can trigger motor programs. Here, we show that this autonomous activity is more frequent in the presence of peptides. Previous studies have also shown that activity of the feeding central pattern generator (CPG) can be initiated by neurons that excite B63, e.g., by cerebral buccal interneuron 2 (CBI-2), a projection neuron that triggers biting-like motor programs. Here, we show that the latency of CBI-2-induced activity is decreased by stimulation of the esophageal nerve (EN) (which releases endogenous SCP). These results, taken together with previous results, indicate that peptides that act divergently to configure network activity additionally act convergently to promote motor program induction. We present data that suggest that this arrangement facilitates brief switches between ingestive and egestive motor activity. © 2025 The Authors.

关键词： central pattern generator invertebrate modulation motor program neuropeptide

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Style transfer-enabled Sim2Real framework for efficient learning of robotic transesophageal echocardiography using simulation

Style transfer-enabled Sim2Real framework for efficient lear...

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2023 International Conference on Biomimetic Intelligence and robotics

作者： Li, Keyu Mao, Xinyu Ye, Chengwei Li, Ang Xu, Yangxin Meng, Max Q.-H. Department of Electronic Engineering The Chinese University of Hong Kong Hong Kong Shenzhen Key Laboratory of Robotics Perception and Intelligence the Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China Shenzhen Research Institute The Chinese University of HongKong Shenzhen518057 China

Recently, various machine learning techniques have been proposed to realize automatic ultrasound (US) image analysis for robotic US acquisition tasks. However, obtaining large amounts of real US images for training is usually expensive or even infeasible in some clinical applications, such as transesophageal echocardiography (TEE). An alternative is to build a simulator to generate synthetic US data for training, but the differences between simulated and real US images may result in poor model performance. This work presents a novel Sim2Real framework to learn robotic US image analysis based on simulated data to address the challenges of limited real US data for robotic TEE. A style transfer model is proposed based on unsupervised contrastive learning to convert real US images into the simulation style. Moreover, a task-relevant model is designed to combine CNNs with vision transformers and trained only on the simulated data to generate task-dependent predictions. We demonstrate the effectiveness of our method in an image regression task to predict the probe position based on US images in robotic TEE. Our results show that using only simulated US data and a small amount of unlabelled real data for training, our method can achieve comparable performance to semi-supervised and fully supervised learning methods. Moreover, the effectiveness of our previously proposed CT-based US image simulation method is also indirectly confirmed. © 2023 The Authors. Published by Elsevier B.V.

关键词： robotics

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Learning Load Flow Analysis in Electric Power Systems: A Case Study in PowerFactory 45

Learning Load Flow Analysis in Electric Power Systems: A Cas...

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45th Jubilee International Convention on Information, Communication and Electronic Technology, MIPRO 2022

作者： Villena-Ruiz, Raquel Honrubia-Escribano, Andres Gomez-Lazaro, Emilio Renewable Energy Research Institute and DIEEAC-ETSII-AB Albacete02071 Spain Department of Automation Electrical Engineering and Electronic Technology Cartagena30202 Spain

ISBN: (纸本)9789532331035

The integration of distributed generation into networks, together with the high variable renewable energy penetration levels, is making current power systems to experience a complex transformation. Under this framework, load flow analyses are required not only to know the operating point of an electrical grid at any given time, but also to plan network operations effectively, conduct contingency analyses and design future expansions. Moreover, load flows constitute the necessary preliminary step to the execution of dynamic simulation analyses. On the other hand, among the software tools used for the conduction of load flow analysis, DIgSILENT PowerFactory (PF) is used in the present paper, since it is one of the most powerful power system analysis packages in the field of electrical engineering. Moreover, it is a tool widely used by network operators and other stakeholders in the electricity sector. Therefore, given the importance of conducting load flow analysis, the present work implements a test network using PF and analyzes its operating point. Our contribution is intended to bring this power system analysis software tool to students, who will be the engineers of the future, and to all researchers, professors or professionals who are not familiar with this commercial tool and have an interest in learning it, thus complementing their competences. Finally, it should also be noted that the test case proposed can be easily replicated at other academic institutions, and that a specific section on the experience of the authors as professors using this software as an educational tool has been included. © 2022 Croatian Society MIPRO.

关键词： Electric load flow

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