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Dielectric Strength of Transformer Oil of Various Qualities

Journal of engineering Science and Technology Review 2021年第1期14卷 201-205页

作者： Rallis, G. Sarathi, R. Danikas, G. Democritus University of Thrace Department of Electrical and Computer Engineering Electric Power Systems Laboratory Xanthi67100 Greece Indian Institute of Technology Madras Department of Electrical Engineering Chennai India

This paper focuses on the effects of dielectric strength on transformer oil reliability and on a reproduction of a voltage breakdown simulation during a dielectric strength test. A typical Foster test cell was used in order to measure the dielectric strength of a variety of oils. For a more complete picture, it requires the identification of more parameters but this paper examines only the above one as a result of the operation of a testing apparatus. It is shown that FEM/FDM analysis provides same information for the electrode gap. Bruce electrode provides highly uniform electric field. Factors such as voltage rate, relaxation time between measurements as well as the shape of the testing electrodes influences the dielectric strength of the liquid. © 2021 School of Science, IHU. All rights reserved.

关键词： Electrodes

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Neural-iLQR: A Learning-Aided Shooting Method for Trajectory Optimization

Neural-iLQR: A Learning-Aided Shooting Method for Trajectory...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Cheng, Zilong Li, Yulin Chen, Kai Duan, Jianghua Ma, Jun Lee, Tong Heng National University of Singapore Department of Electrical and Computer Engineering Singapore The Hong Kong University of Science and Technology Department of Mechanical and Aerospace Engineering Hong Kong The Hong Kong University of Science and Technology Division of Emerging Interdisciplinary Areas Hong Kong Robotics and Autonomous Systems Thrust Guangzhou China Hkust Shenzhen-Hong Kong Collaborative Innovation Research Institute Shenzhen China

ISBN: (纸本)9798350325706

Iterative linear quadratic regulator (iLQR) has gained wide popularity in addressing trajectory optimization problems with nonlinear system models. However, as a model-based shooting method, it relies heavily on an accurate system model to update the optimal control actions and the trajectory determined with forward integration, thus becoming vulnerable to inevitable model inaccuracies. Recently, substantial research efforts in learning-based methods for optimal control problems have been progressing significantly in addressing unknown system models, particularly when the system has complex interactions with the environment. Yet an incremental machine learning method or a deep neural network is normally required to fit a substantial scale of sampling motion data. Existing works mainly focus on capturing the whole picture of the motion pattern from trial data (which is essentially noisy and hard to identify with any simple predefined structures) for complex robot systems. Instead, we present Neural-iLQR, a learning-aided shooting method over the unconstrained control space, in which a neural network with a simple structure is used to drive the optimization process towards optimal direction. Through comprehensive evaluations on two illustrative control tasks using three different kinds of lightweight network structures, the proposed method is shown to outperform the conventional iLQR significantly with fast and continuous cost convergence in the presence of inaccuracies in system models, which demonstrates the generalizability and robustness of the proposed learning-aided method. © 2023 IEEE.

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Cyberbullying Classification Using a Natural Language Processing and Machine Learning Techniques

Cyberbullying Classification Using a Natural Language Proces...

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2024 International Conference on Advances in Computing Research on Science engineering and Technology, ACROSET 2024

作者： Mankar, Nikhilesh Pramod Mahure, Tushar Joshi, Anushka A. Pande, Sagar Dhanraj Khamparia, Aditya Nathe, Ankita Shrikrushna Turjman, Fadi Al Pune India Independent Researcher Amravati India Babasaheb Bhimrao Ambedkar University Department of Computer Science Amethi India Kavikulguru Institute of Technology and Science Department of Information Technology Nagpur India Ai and Robotics Institute Near East University Artificial Intelligence Software Information Systems Engineering Departments Nicosia Cyprus University of Kyrenia Research Center for Ai and IoT Faculty of Engineering Kyrenia Turkey

ISBN: (纸本)9798350388800

The usage of online entertainment has increased dramatically after some time with the enhancement of the Internet and has turned into the most compelling systems administration stage in this century. Notwithstanding, the upgrade of a social network regularly makes adverse consequences on society that add to two or three terrible peculiarities, for example, online maltreatment, badgering cyberbullying, cybercrime, and internet savaging. Cyberbullying habitually prompts genuine mental and actual misery, especially for ladies and kids, and even occasionally make them to use self-destruction methods. Multiple number of episodes have as of late happened worldwide because of online badgering, like sharing private talks, bits of hearsay, and sexual comments. In this way, the distinguishing proof of harassing messages or messages via online entertainment has gained a significant measure for development for consideration with in scientists. The ground or motive for this exploration is to create a full-fledged way or a method to differentiate online harmful and tormenting drafts by combining various methods and techniques in the domain of natural language processing and AI © 2024 IEEE.

关键词： Adversarial machine learning

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MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutional Networks for 3D Human Pose Estimation

MSMB-GCN: Multi-scale Multi-branch Fusion Graph Convolutiona...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Ji, Shanshan Meng, Qiwei Wang, Wen Lin, Zheyuan Li, Te Wan, Minhong Zhang, Chunlong Gu, Jason Research Institute of Interdisciplinary Innovation Zhejiang Laboratory Research Center for Intelligent Robotics Hangzhou311100 China Dalhousie University Department of Electrical and Computer Engineering HalifaxNSB3H 4R2 Canada

ISBN: (纸本)9798350325706

In human-robot interaction (HRI), human pose estimation is a necessary technology for the robot to perceive the dynamic environment and make interactive actions. Recently, graph convolutional networks (GCNs) have been increasingly used for 2D to 3D pose estimation tasks since the skeleton topologies can be viewed as graph structures. In this paper, we propose a novel graph convolutional network architecture, Multi-scale Multi-branch Fusion Graph Convolutional Networks (MSMB-GCN), for 3D Human Pose Estimation(3D HPE) task. The proposed model consists of multiple GCN blocks with a multi-branch architecture. This multi-branch architecture enables the model to get multi-scale features for human skeletal representations. The group of GCN blocks, which has strong multi-level feature extraction capabilities, allows the model to learn global and local features, lower-level and higher-level features. Experiment results on the HumanPose benchmark demonstrate that our model outperforms the state-of-the-art and ablation studies validate the effectiveness of our approach. © 2023 IEEE.

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RTV Silicone Rubber Coatings for Outdoor Insulators: A Concise Review of Some Factors Affecting their Behavior and Some Comments

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Journal of engineering Science and Technology Review 2021年第1期14卷 163-169页

作者： Sarathi, R. Danikas, M.G. Indian Institute of Technology Madras Department of Electrical Engineering High Voltage Laboratory Chennai India Democritus University of Thrace Department of Electrical and Computer Engineering Power Systems Laboratory Xanthi67100 Greece

Traditional outdoor insulators are made of glass and porcelain. However, although such insulators have been used for many decades, they present some drawbacks. Glass and porcelain insulators seem to be more prone to surface discharges and flashover when they age and water droplets are more likely to spread on their surface, causing thus dry zones and consequently conditions for surface discharging and flashover. Room Temperature Vulcanized (RTV) coatings spayed on the aforementioned traditional insulators seem to remedy such problems because such coatings increase the hydrophobicity. In this paper, a short review on RTV coatings is given together with some thoughts and comments regarding the so-called early stage of aging. © 2021 School of Science, IHU. All rights reserved.

关键词： Contact angle

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Advanced Signal Processing For IoT Device Communication In Smart Homes 3

Advanced Signal Processing For IoT Device Communication In S...

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3rd IEEE International Conference on Automation and Computation, AUTOCOM 2025

作者： Malini, M. Jashva, Munigeti Benjmin Banu, M.Sheerin Jayanthi, N. Umarani, C. Babu, T.Harish Akshaya College of Engineering and Technology Department of Computer Science and Business Systems Coimbatore India Malla Reddy University Department of Cse Telangana Hyderabad India R.M.K. Engineering College Department of Information Technology Chennai India Rmk College of Engineering and Technology Department of Physics Chennai India Alagappa Chettiar Government College of Engineering and Technology Department of Cse TamilNadu Karaikudi India Sethu Institute of Technology Electrical and Electronics Engineering India

ISBN: (纸本)9798331542375

This work focuses on intelligent home ventilation systems since they provide a fresh approach to the problem. By using renewable energy sources and cloud-based ANFIS architecture, the approach seeks to lower the energy consumption. The study focuses on the creation of a cloud-based cooling system able to maximize energy economy without sacrificing the degree of comfort inherent in the construction. The most important problem is not enough study has been done on how to include renewable energy sources into house cooling control systems. The described method combines a ANFIS system functioning in the cloud with solar power. This system may change the cooling power output in response to occupancy levels, humidity, and temperature as external factors. Conversely, unlike conventional cooling systems, the results imply that 10% of energy consumption might be cut without sacrificing the degree of comfort inside the structure. It is shown via the ANFIS approach that cooling power can be precisely controlled - a essential ability to maximize the use of ecologically friendly energy sources. © 2025 IEEE.

关键词： Smart homes

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Updating Robot Safety Representations Online from Natural Language Feedback

arXiv

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arXiv 2024年

作者： Santos, Leonardo Li, Zirui Peters, Lasse Bansal, Somil Bajcsy, Andrea School of Engineering Federal University of Minas Gerais Brazil Electrical and Computer Engineering University of Rochester United States Department of Cognitive Robotics Delft University of Technology Netherlands Electrical and Computer Engineering University of Southern California United States Robotics Institute Carnegie Mellon University United States

Robots must operate safely when deployed in novel and human-centered environments, like homes. Current safe control approaches typically assume that the safety constraints are known a priori, and thus, the robot can precompute a corresponding safety controller. While this may make sense for some safety constraints (e.g., avoiding collision with walls by analyzing a floor plan), other constraints are more complex (e.g., spills), inherently personal, context-dependent, and can only be identified at deployment time when the robot is interacting in a specific environment and with a specific person (e.g., fragile objects, expensive rugs). Here, language provides a flexible mechanism to communicate these evolving safety constraints to the robot. In this work, we use vision language models (VLMs) to interpret language feedback and the robot’s image observations to continuously update the robot’s representation of safety constraints. With these inferred constraints, we update a Hamilton-Jacobi reachability safety controller online via efficient warm-starting techniques. Through simulation and hardware experiments, we demonstrate the robot’s ability to infer and respect language-based safety constraints with the proposed approach. Copyright © 2024, The Authors. All rights reserved.

关键词： Feedback

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A Survey on Group Fairness in Federated Learning: Challenges, Taxonomy of Solutions and Directions for Future Research

arXiv

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arXiv 2024年

作者： Salazar, Teresa Araújo, Helder Cano, Alberto Abreu, Pedro Henriques Centre for Informatics and Systems Department of Informatics Engineering University of Coimbra Coimbra3030-790 Portugal Institute of Systems and Robotics Department of Electrical and Computer Engineering University of Coimbra Coimbra3030-790 Portugal Virginia Commonwealth University RichmondVA23284 United States

Group fairness in machine learning is a critical area of research focused on achieving equitable outcomes across different groups defined by sensitive attributes such as race or gender. Federated learning, a decentralized approach to training machine learning models across multiple devices or organizations without sharing raw data, amplifies the need for fairness due to the heterogeneous data distributions across clients, which can exacerbate biases. The intersection of federated learning and group fairness has attracted significant interest, with 47 research works specifically dedicated to addressing this issue. However, no dedicated survey has focused comprehensively on group fairness in federated learning. In this work, we present an in-depth survey on this topic, addressing the critical challenges and reviewing related works in the field. We create a novel taxonomy of these approaches based on key criteria such as data partitioning, location, and applied strategies. Additionally, we explore broader concerns related to this problem and investigate how different approaches handle the complexities of various sensitive groups and their intersections. Finally, we review the datasets and applications commonly used in current research. We conclude by highlighting key areas for future research, emphasizing the need for more methods to address the complexities of achieving group fairness in federated *** Codes 68T01 © 2024, CC BY.

关键词： Federated learning

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Noise Analysis of Memristors: Single Device and Crossbar array

Noise Analysis of Memristors: Single Device and Crossbar arr...

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IEEE National Conference on Aerospace and Electronics (NAECON)

作者： Muana Kasongo Trond Ytterdal John J. Lee Maher Rizkalla Mukesh Kumar Electrical and Computer Engineering Department Purdue University Indianapolis USA Electronic systems Department Norwegian Institute of Technology Trondheim Norway Electrical Engineering Department Indian Institute of Technology Indore Indore India

ISBN: (数字)9798350367621

ISBN: (纸本)9798350367638

An extensive amount of research on noise has been conducted regarding traditional passive and active electronic devices. Being new passive electronic devices, memristors still lack systematic noise analysis and measurements in the literature. In this paper, we propose the measurement of the signal-to-noise ratio, the noise figure, the noise power spectral density in both a memristor as a single device and in a memristor-based crossbar structure, and the bandwidth. The results show that memristor devices' behavior under the influence of noise can be analyzed by relying on some strong existing assumptions based on linear circuits where the memristor device is treated instantaneously as a linear resistor, a voltage-driven memristor device is modeled as a voltage source in series with a noiseless resistor $\mathrm{R}$ with uniform white power spectral density, and the root mean square voltage of the noise is equivalent to the standard deviation of the zero-mean Gaussian PDF governing the thermal noise. This experiment paves the way for in-depth memristor noise investigations that could lead to the derivation of a generalized noise model tailor-made for memristor devices.

关键词： Noise figure Thermal resistance Noise Memristors Bandwidth Linear circuits White noise

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Incentive Designs for Learning Agents to Stabilize Coupled Exogenous systems

Incentive Designs for Learning Agents to Stabilize Coupled E...

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IEEE Conference on Decision and Control

作者： Jair Certório Nuno C. Martins Richard J. La Murat Arcak Department of Electrical and Computer Engineering Institute for Systems Research University of Maryland College Park College Park MD USA Department of Electrical Engineering and Computer Science University of California Berkeley CA USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

We consider a large population of learning agents noncooperatively selecting strategies from a common set, influencing the dynamics of an exogenous system (ES) we seek to stabilize at a desired equilibrium. Our approach is to design a dynamic payoff mechanism capable of shaping the population’s strategy profile, thus affecting the ES’s state, by offering incentives for specific strategies within budget limits. Employing system-theoretic passivity concepts, we establish conditions under which a payoff mechanism can be systematically constructed to ensure the global asymptotic stability of the ES’s equilibrium. In comparison to previous approaches originally studied in the context of the so-called epidemic population games, the method proposed here allows for more realistic epidemic models and other types of ESs, such as predator-prey dynamics. The stability of the equilibrium is established with the support of a Lyapunov function, which provides useful bounds on the transient states.

关键词： Epidemics Asymptotic stability Games Stability analysis Transient analysis Lyapunov methods Convergence Context modeling

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