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IEEE International Conference on Soft Robotics (RoboSoft)

作者： Julien Mellet Andrea Berra Achilleas Santi Seisa Viswa Sankaranarayanan Udayanga G.W.K.N. Gamage Miguel Ángel Trujillo Soto Guillermo Heredia George Nikolakopoulos Vincenzo Lippiello Fabio Ruggiero Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Robotics and AI Team Electrical and Space Engineering LuleåUIniversity of Technology Luleå Sweden Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors' integrity. Notably, the arm's damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction.

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Adaptive Vehicle control System for Improved Safety With Image Processing

Adaptive Vehicle Control System for Improved Safety With Ima...

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IEEE International Conference on Emerging & Sustainable Technologies for Power & ICT in a Developing Society (NIGERCON)

作者： N. Vijayalakshmi L. Priya S. Ramaraj Menakadevi Nanjundan Satheesh Kumar Palanisamy Ayodeji Olalekan Salau Sepiribo Lucky Braide Department of Computer Science and Engineering Alliance School of Advanced Compuitng Alliance University Bangalore Department of ECE Sri Eshwar College of Engineering (Autonomous) Kinathukadavu Coimbatore Tamilnadu India Department of Computer Science and Engineering Karpagam College of Engineering India Department of ECE Karpagam Institute of Technology Coimbatore Department of ECE BMS Institute of Technology and Management Bengaluru Karnataka India Department of Electrical/Electronics and Computer Engineering Afe Babalola University Ado Ekiti Nigeria Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Department of Electrical and Electronics Engineering Rivers State University Port Harcourt Nigeria

ISBN: (数字)9798331542559

ISBN: (纸本)9798331542566

We propose the development of an Adaptive Vehicle control (AVC) system using readily available components such as a Raspberry Pi microcontroller, a motor, a motor driver, and a Raspberry Pi camera. This system aims to enhance safety and convenience by automatically adjusting vehicle speed based on surrounding traffic conditions and recognizing relevant road signs like school zones, speed limits, and hospital zones. The process begins with real-time image capture using the Raspberry Pi camera module. These images are then analyzed using Python-based computer vision algorithms, primarily leveraging OpenCV. The image processing pipeline employs techniques such as object detection and template matching to identify specific road signs within the captured frames. Upon sign detection, the system interprets their meanings and determines the appropriate speed limits associated with them. Using the information from the detected signs and their corresponding speed limits, the system regulates the motor connected to the vehicle's throttle through GPIO pins on the Raspberry Pi. By adjusting the motor's speed, the AVC system ensures compliance with the specified speed limits across various road conditions. Safety measures are paramount, with fail-safe mechanisms implemented to prevent accidents in case of system errors or malfunctions. This AVC system offers a cost-effective and adaptable solution for integrating intelligent cruise control capabilities into vehicles. Leveraging the computational power of the Raspberry Pi and the versatility of image processing techniques, the system can adapt to diverse driving environments, thereby enhancing overall driving safety. This project contributes to the progression of smart transportation systems and lays the foundation for further advancements in autonomous driving technologies.

关键词： Adaptive systems Roads Pipelines Motors Cameras Real-time systems Safety Smart transportation Cruise control Vehicles

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autonomous Voltage Stabilization in Hybrid Energy Grids with Reinforcement Learning

Autonomous Voltage Stabilization in Hybrid Energy Grids with...

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Trends in Material Science and Inventive Materials (ICTMIM), International Conference on

作者： Saranya R K.R. Jansi M. Gowthami Priya N T. R. GaneshBabu S Murugan Department of Computer Science and Engineering Annapoorana Engineering College (Autonomous) Salem Tamil Nadu India Department of Computing Technologies SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India Department of Computer Application Vinayaka Mission's Kirupananda Variyar Arts and Science College Vinayaka Mission’s Research Foundation (Deemed to be University) Salem Tamil Nadu India Department of Electrical and Electronics Engineering Easwari Engineering College Chennai Tamil Nadu India Department of Electronics and Communication Muthayammal Engineering College (Autonomous) Namakkal Tamil Nadu India Department of Computer Science and Engineering Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai Tamil Nadu India

ISBN: (数字)9798331501488

ISBN: (纸本)9798331501495

The growing incorporation of renewable energy sources into hybrid energy networks requires creative ways to maintain voltage stability. This work investigates using reinforcement learning (RL) algorithms to autonomously regulate voltage levels in hybrid energy systems that include solar, wind, and traditional power sources. It presents an innovative RL-based controller that dynamically modulates the output of distributed energy resources (DERs) in response to variable load demands and generation fluctuations. This research addresses voltage fluctuations in hybrid energy networks using RL. The proposed RL-based system improves voltage stability, flexibility, and efficiency, outperforming traditional techniques in both simulations and practical applications. The RL agent uses real-time data to learn optimum control techniques to reduce voltage variances successfully. The efficacy of the proposed system is assessed by simulations on a benchmark microgrid model, revealing substantial improvements in voltage regulation and system resilience relative to traditional control approaches. The results demonstrate that the RL-based method improves voltage stability and optimizes overall energy utilization efficiency within the grid. It emphasizes the possibility of incorporating AI-driven solutions in managing hybrid energy systems, facilitating the development of more sustainable and dependable energy infrastructures.

关键词： Adaptation models Wind Fluctuations Computational modeling Reinforcement learning Microgrids Power system stability Stability analysis Real-time systems Voltage control

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Weighted AoI Minimization for RIS-Assisted Multi-User mmWave Communications

Weighted AoI Minimization for RIS-Assisted Multi-User mmWave...

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International Conference on Communication Technology (ICCT)

作者： Ziqi Guo Yong Niu Shuxian Wen Ying Fang Zhu Han Shiwen Mao Shunji Guan Ning Wang Bo Ai State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Department of Electrical and Computer Engineering University of Houston Houston TX USA Department of Electrical and Computer Engineering Auburn University Auburn AL USA School of Information Engineering Zhengzhou University Zhengzhou China

In this paper, we focus on a reconfigurable intelligent surface (RIS)-assisted millimeter-wave (mmWave) communications system, where a RIS is deployed to create reliable reflection links and alleviate multi-user interference. In the system, each user equipment (UE) is concerned with the freshness of the information received from the base station (BS) and has a certain transmission requirement. The age of information (AoI) is adopted as the metric for information freshness and on this basis, we define the weighted AoI to take into account the satisfaction of UE transmission requirements. Then, an average weighted AoI minimization problem is formulated by jointly optimizing the precoding, the discrete RIS phase shifts, and the scheduling strategy. The problem is decomposed into two subproblems. For the first precoding and RIS design subproblem, we aim to satisfy the transmission requirements of the scheduled UEs with minimum power consumption under a given scheduling strategy. We use the zero-force (ZF)-based precoder and adopt the local search method for the RIS design. For the second scheduling strategy design subproblem, we propose a heuristic scheduling algorithm to minimize the system's average weighted AoI. Simulation results verify the effectiveness of our proposed method over several baseline schemes.

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A Sensorless Method based on Zero-sequence Back-EMF for Five-phase Open-end Winding PMSM with Deadtime Compensation

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IEEE Transactions on Energy Conversion 2025年

作者： Jiang, Chunyang Wang, Yingjie Liu, Hongchen Wheeler, Pat Bao, Danyang Northeast Forestry University College of Computer and Control Engineering Harbin150001 China Harbin Institute of Technology School of Electrical Engineering and Automation Harbin150001 China University of Nottingham Department of Electrical and Electronics Engineering NottinghamNG7 2RD United Kingdom Shenzhen Polytechnic University School of Mechanical and Electrical Engineering Shenzhen518000 China

This paper proposes a sensorless method using the zero-sequence back electromotive force (ZSB-EMF) for five-phase open-end winding permanent magnet synchronous machine (OEW-PMSM) with common dc bus. The output of the zero-sequence controller, which is applied to suppress the zero-sequence current (ZSC), is used to reconstruct the ZSB-EMF. Then the position of the rotor is extracted from the output signal of the zero-sequence controller by single phase locked loop. A compensation of the deadtime is proposed to eliminate the negative effect for the position estimation and increase the effective modulation index. The proposed method needs no parameters of the motor, like the resistance, d-q inductance, which are vulnerable and might affect the accuracy of the estimation for the conventional sensorless methods based on the ZSB-EMF. It is easy to implement and the resistance network and complex calculation of conventional methods are also avoided. Experimental results on the five-phase open-end winding PMSM drive system are shown to validate the proposed sensorless method. © 1986-2012 IEEE.

关键词： Permanent magnets

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Two-Player Multiagent Graphical Games with Reinforcement Learning*

Two-Player Multiagent Graphical Games with Reinforcement Lea...

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Industrial Cyber-Physical Systems (ICPS)

作者： Bosen Lian Jiacheng Wu Electrical and Computer Engineering Department Auburn University Auburn AL USA State Key Laboratory of Industrial Control Technology Zhejiang University Hangzhou China

ISBN: (数字)9798350363012

ISBN: (纸本)9798350363029

This paper studies the synchronization problem of two-player multiagent systems through reinforcement learning methods. A Nash-minmax strategy is formulated, where the interactions of two players in the same agent are non-zero-sum, while interactions of players between agents are zero-sum games. We propose an offline model-based reinforcement learning algorithm to identify Nash solutions for players within each agent, as well as the worst control solutions for players in neighboring antagonistic agents. On this basis, a data-driven off-policy algorithm is provided to alleviate the requirement for accurate system dynamics in the offline algorithm. Besides, the convergence of the proposed algorithms is analyzed. Finally, simulation results verify the effectiveness of the designed algorithms.

关键词： System dynamics Heuristic algorithms Simulation Games Reinforcement learning Cyber-physical systems Synchronization

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Boosting Reweighting Convolutional Neural Network for Microscopic Images to Identify and Classify Malaria Parasites

Boosting Reweighting Convolutional Neural Network for Micros...

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Software, Systems and Information Technology (SSITCON), International Conference on

作者： E.S Challaraj Emmanuel Saif O. Husain K. Butchi Raju G Ravi R. Venkatasubramanian Department of Life Sciences Kristu Jayanti College (Autonomous) Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of Computer Science and Engineering Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India Department of Electronics and Communication Engineering Sona College of Technology Salem India Department of Electrical and Electronics Engineering New Prince Shri Bhavani College of Engineering and Technology Chennai India

ISBN: (数字)9798350352931

ISBN: (纸本)9798350352948

Malaria is a critical health condition that affects both sultry and frigid region worldwide, its rise to millions of cases disease. The malaria is caused by parasites through to pass in the human blood cells, damage them over the time and occur the noise because it blends in the human blood cells. Therefore, it is identifying by using microscope Image to exanimated the blood cells. This paper, proposed Boosting Reweighting Convolutional Neural Network (BR-CNN) technique efficiently analysis the region to malaria parasites classify the disease image. Data obtained from the NIH Malaria dataset and pre-processing utilized remove unwanted noise in the image and normalization of the feature vector. The feature extraction involved the ResNet 50 utilized residual connections, which allows gradients to flow through the network, effectively extracted the malaria parasites. The Classification utilized BR-CNN technique is automatically extract features from the input blood cell image. The CNN processes the image through layer of the convolutional operations and non-linear activation to build a hierarchical representation of data. The performance of proposed model evaluated the various metrics such as accuracy, precision, recall and sensitivity metrics on NIH dataset.

关键词： Accuracy Malaria Microscopy Noise Cells (biology) Feature extraction Boosting National Institutes of Health Convolutional neural networks Blood

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Secondary Frequency control of Dual-Port Grid Forming control

Secondary Frequency Control of Dual-Port Grid Forming Contro...

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Power Tech Conference

作者： Irina Subotic Dominic Groß Automatic Control Laboratory ETH Zürich Switzerland Department of Electrical and Computer Engineering University of Wisconsin-Madison USA

Grid-forming converters are widely envisioned to be the cornerstone of future converter-dominated power systems. However, standard grid forming (GFM) control strategies assume a fully controllable source with enough headroom behind the converter and only implicitly address renewable generation limits through the converter limits. This can lead to instabilities on time scales of both primary and secondary frequency control and jeopardize the safe and reliable operation of electric power systems. In this work, we leverage the recently proposed dual-port GFM control that maps power imbalances in the grid to the power generation interfaced by the power converter. We show that this mechanism allows for considering and transparently addressing limits of renewable generation (e.g., solar photovoltaics and wind) in primary and secondary frequency control. We illustrate that renewable generation using dual-port GFM control can directly integrate into prevailing secondary control methods such as automatic generation control (AGC). Moreover, we discuss the limitations of standard AGC when one or more areas of a power system are dominated by renewable generation and propose an anti-windup strategy to address the power generation limits of renewables. Finally, we verify our findings in a time-domain, electromagnetic transient (EMT) simulation.

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Assisted Physical Interaction: autonomous Aerial Robots with Neural Network Detection, Navigation, and Safety Layers

Assisted Physical Interaction: Autonomous Aerial Robots with...

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International Conference on Unmanned Aircraft Systems (ICUAS)

作者： Andrea Berra Viswa Narayanan Sankaranarayanan Achilleas Santi Seisa Julien Mellet Udayanga G.W.K.N. Gamage Sumeet Gajanan Satpute Fabio Ruggiero Vincenzo Lippiello Silvia Tolu Matteo Fumagalli George Nikolakopoulos Miguel Ángel Trujillo Soto Guillermo Heredia CATEC Advanced Center for Aerospace Technologies Seville Spain Department of Computer Electrical and Space Engineering Robotics and AI Team Luleå University of Technology Luleå Sweden Department of Electrical Engineering and Information Technology PRISMA Lab University of Naples Federico II Naples Italy Department of Electrical Engineering and Photonics Automation and Control Group Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

ISBN: (数字)9798350357882

ISBN: (纸本)9798350357899

The paper introduces a novel framework for safe and autonomous aerial physical interaction in industrial settings. It comprises two main components: a neural network-based target detection system enhanced with edge computing for reduced onboard computational load, and a control barrier function (CBF)-based controller for safe and precise maneuvering. The target detection system is trained on a dataset under challenging visual conditions and evaluated for accuracy across various unseen data with changing lighting conditions. Depth features are utilized for target pose estimation, with the entire detection framework offloaded into low-latency edge computing. The CBF-based controller enables the UAV to converge safely to the target for precise contact. Simulated evaluations of both the controller and target detection are presented, alongside an analysis of real-world detection performance.

关键词： Visualization Pose estimation Neural networks Lighting Object detection autonomous aerial vehicles Aircraft navigation

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Design of a Flexible Robot Arm for Safe Aerial Physical Interaction

arXiv

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arXiv 2024年

作者： Mellet, Julien Berra, Andrea Seisa, Achilleas Santi Sankaranarayanan, Viswa Gamage, Udayanga G.W.K.N. Soto, Miguel Ángel Trujillo Heredia, Guillermo Nikolakopoulos, George Lippiello, Vincenzo Ruggiero, Fabio PRISMA Lab Department of Electrical Engineering and Information Technology University of Naples Federico II Naples Italy CATEC Advanced Center for Aerospace Technologies Seville Spain Robotics and AI Team Department of Computer Electrical and Space Engineering LuleåUniversity of Technology Luleå Sweden Automation and Control Group Department of Electrical Engineering and Photonics Technical University of Denmark Denmark Robotics Vision and Control Group School of Engineering University of Seville Seville Spain

This paper introduces a novel compliant mechanism combining lightweight and energy dissipation for aerial physical interaction. Weighting 400 g at take-off, the mechanism is actuated in the forward body direction, enabling precise position control for force interaction and various other aerial manipulation tasks. The robotic arm, structured as a closed-loop kinematic chain, employs two deported servomotors. Each joint is actuated with a single tendon for active motion control in compression of the arm at the end-effector. Its elasto-mechanical design reduces weight and provides flexibility, allowing passive-compliant interactions without impacting the motors’ integrity. Notably, the arm’s damping can be adjusted based on the proposed inner frictional bulges. Experimental applications showcase the aerial system performance in both free-flight and physical interaction. The presented work may open safer applications for Micro Aerial Vehicle (MAV) in real environments subject to perturbations during interaction. © 2024, CC BY.

关键词： End effectors

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