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Newton-Euler Modeling of Robot Dynamics Using Geometric Algebra

Newton-Euler Modeling of Robot Dynamics Using Geometric Alge...

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International Conference on Methods and Models in Automation and Robotics (MMAR)

作者： Eduardo Bayro-Corrochano Daniel Alvarado-Mejia Institute of Automatic Control and Robotics Poznan University of Technology Dept. Electrical Engineering and Computer Science CINVESTAV Guadalajara Mexico

ISBN: (数字)9798350362343

ISBN: (纸本)9798350362350

In this work, we present the modeling of the dynamics of a robot manipulator using the Newton-Euler algorithm in the conformal algebra framework. The modeling of the dynamics of robot manipulators is currently done using the Euler-Lagrange formulation which is a batch type of computation. In contrast, in this paper, we propose a recursive algorithm for the modeling of the dynamics of robot manipulators using the Newton-Euler algorithm in the conformal geometric algebra framework.

关键词： Algebra Heuristic algorithms Computational modeling Optimal control Motors Real-time systems Mathematical models

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An Explainable Machine Learning Approach for Heartbeat Classification Through Signal-Based Features

An Explainable Machine Learning Approach for Heartbeat Class...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Paolo Sorino Gianluca Colonna Domenico Lofù Tommaso Colafiglio Angela Lombardi Fedelucio Narducci Tommaso Di Noia Dept. of Electrical and Information Engineering (DEI) Politecnico di Bari Bari Italy Dept. of Computer Automatic and Management Engineering (DIAG) Sapienza Università di Roma Roma Italy

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

Cardiovascular disease (CVD) is a general term referring to several heart or blood vessels abnormality. Heart failure (HF), directly associated to (CVD), is a significant global health problem as well as the leading cause of morbidity and mortality. The early detection of this condition is crucial for patient health. Traditional diagnostic methods for HF, such as history taking and physical examination, are often insufficient and require the use of advanced techniques such as Electrocardiogram (ECG). This study aims to extract temporal and morphological features from (ECG) signals and compare different Machine Learning (ML) classification models to enable rapid diagnosis and provide interpretable predictions. Specifically, we propose a Light Gradient Boosting (LGBM) model that can discriminate Normal Sinus Rhythm (NSR) and Arrhythmia (ARR) with a high accuracy of 0.99, achieving a Precision of 1.00, Recall of 0.99, and f1-score of 0.99 in the (NSR) class, and Precision of 0.99, Recall of 1.00, and f1-score of 0.99 in the (ARR) class, respectively. In addition, eXplainable Artificial Intelligence (XAI) analysis is performed to explain the model predictions.

关键词： Heart Heart beat Explainable AI Mortality Predictive models Electrocardiography Feature extraction Rhythm Cardiovascular diseases History

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Distributionally robust optimization as a scalable framework to characterize extreme value distributions 24

Distributionally robust optimization as a scalable framework...

引用

Proceedings of the Fortieth Conference on Uncertainty in Artificial Intelligence

作者： Patrick Kuiper Ali Hasan Wenhao Yang Yuting Ng Hoda Bidkhori Jose Blanchet Vahid Tarokh Dept. of Electrical and Computer Engineering Duke University Durham North Carolina Dept. of Management Science and Engineering Stanford University Stanford California Computational and Data Sciences Dept. George Mason University Fairfax Virginia

The goal of this paper is to develop distributionally robust optimization (DRO) estimators, specifically for multidimensional Extreme Value Theory (EVT) statistics. EVT supports using semi-parametric models called max-stable distributions built from spatial Poisson point processes. While powerful, these models are only asymptotically valid for large samples. However, since extreme data is by definition scarce, the potential for model misspecification error is inherent to these applications, thus DRO estimators are natural. In order to mitigate over-conservative estimates while enhancing out-of-sample performance, we study DRO estimators informed by semi-parametric max-stable constraints in the space of point processes. We study both tractable convex formulations for some problems of interest (e.g. CVaR) and more general neural network based estimators. Both approaches are validated using synthetically generated data, recovering prescribed characteristics, and verifying the efficacy of the proposed techniques. Additionally, the proposed method is applied to a real data set of financial returns for comparison to a previous analysis. We established the proposed model as a novel formulation in the multivariate EVT domain, and innovative with respect to performance when compared to relevant alternate proposals.

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Strategic Placement of FACTS Devices for Optimizing Power Transmission Efficiency and Stability

Strategic Placement of FACTS Devices for Optimizing Power Tr...

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International Symposium on Digital Forensic and Security (ISDFS)

作者： Blake Pickett Asaf Varol Dept. of Engineering Management & Technology College of Engineering and Computer Science University of Tennessee at Chattanooga Chattanooga TN US Department of Computer Engineering College of Engineering & Natural Sciences Maltepe University Istanbul Türkiye

ISBN: (数字)9798331509934

ISBN: (纸本)9798331509941

Flexible AC Transmission Systems (FACTS) devices play a critical role in enhancing power transmission efficiency and stability within modern electrical grids. This study investigates the strategic placement of two key FACTS technologies—Thyristor-controlled Series Capacitors (TCSC) and Static Synchronous Compensators (STATCOM)—to optimize power flow, voltage regulation, and reactive power compensation. Using MATLAB Simulink modeling, a 200kilometer transmission line is simulated with different configurations to assess performance impacts based on device type and location. The analysis reveals that TCSC devices significantly improve power transfer by reducing line reactance and enhancing voltage stability, while STATCOM devices provide superior reactive power support and contribute to overall system stability. Comparative evaluations demonstrate that placing both devices at the sending end of the transmission line yields optimal results in terms of efficiency, power loss reduction, and operational resilience. Additionally, the study addresses the potential environmental implications of deploying FACTS devices near high-emission generation sources, highlighting the need for sustainable integration strategies. These insights offer practical guidance for utilities and system planners aiming to modernize transmission infrastructure through targeted technological interventions.

关键词： Performance evaluation Thyristors Reactive power Power transmission lines Software packages Power system stability Propagation losses Stability analysis Automatic voltage control Power capacitors

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Dynamic Topology Optimization for Efficient and Decentralised Federated Learning

Dynamic Topology Optimization for Efficient and Decentralise...

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IEEE International Conference on Big Data

作者： Danilo Menegatti Alessandro Giuseppi Cecilia Poli Antonio Pietrabissa Dept. of Computer Control and Management Engineering Sapienza University of Rome Rome Italy National Centre for Innovative Technologies in Public Health Department Istituto Superiore di Sanità Rome Italy

ISBN: (数字)9798350362480

ISBN: (纸本)9798350362497

Topology optimization in decentralised federated learning settings enables the design of policies aimed at minimizing the number of communication rounds needed to reach algorithmic convergence. Given a federation of autonomous agents, finding the optimal topology which guarantees that the underlying graph is connected is still an open issue. This paper proposes a novel energy-aware topology optimization algorithm with the goal to derive an optimal topology which maximizes the algebraic connectivity of the corresponding graph in presence of energy and communication constraints. The effectiveness of the proposed approach is validated in the context of a consensus-based federated learning algorithm over an e-Health scenario.

关键词： Federated learning Heuristic algorithms Big Data Autonomous agents Topology Optimization Convergence

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Safety and Security Dependencies for Gridshield

Safety and Security Dependencies for Gridshield

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IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT Europe)

作者： Reza Soltani Baver Ozceylan Milan Lopuhaä-Zwakenberg Christina Kolb Gerwin Hoogsteen Dept. of Electrical Engineering Mathematics and Computer Science University of Twente Enschede the Netherlands Dept. of Behavioural Management and Social Sciences University of Twente Enschede the Netherlands

ISBN: (数字)9798350390421

ISBN: (纸本)9798350390438

Present day cyber-physical systems, such as the Smart Grid, lead to the integration of multiple sub-systems into one single intertwined system. Such systems are characterized by many inter-dependencies between these sub-systems. This makes it complex to correctly assess the impact of new defence mechanisms with respect to the safety and security of the system as a whole. Existing formalisms such as fault and attack trees cannot describe the full system *** paper presents a novel integrated model, namely the Attack-Fault-Defence Tree (AFDT), and tools to analyse such cyber-physical systems. The presented visual representation allows experts from various disciplines to discuss system dependencies together. In addition, we also present how minimum cut sets can be derived to formally quantify how the safety and security of the overall system is enhanced with the implementation of new defences. The presented AFDT is applied to the Gridshield concept, a novel defence mechanism to prevent grid overloading in power grids due to simultaneous charging of electric vehicles.

关键词： Visualization Analytical models Europe Cyber-physical systems Electric vehicles Safety Smart grids Security

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IR Sensor Based Accident Prevention System for Hilly Areas

IR Sensor Based Accident Prevention System for Hilly Areas

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Singh, Jay Datta, Priyanka Kumar, Nagendra Sharma, Kailash Saxena, Abhinav Gupta, Amit Ambikapathy, A. Gl Bajaj Institute of Technology & Management Eee Department Gr Noida India Jss Academy of Technical Education Dept. of Eee U.P. Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Galgotias College of Engineering and Technology Dept. of Eee Gr. Noida India

ISBN: (纸本)9798350323887

This paper provide overview of the model which describe how to minimize the road accident on the curve roads by proposing a model which uses IR sensors as sensing elements for the vehicles coming from either side of the road IR sensor is connected with Arduino Uno software to alert drivers about the vehicles coming from either side of the road also Author have used 16∗ 2 counter to count the vehicles at the turning points to enhance the accuracy at the mountain roads the Arduino sense the signal from the IR sensors which commands the buzzer to start alarming the driver along with RGB LED present on either side of the road we have also use the LDR sensors for a street lighting system at the curve point which get activated at night automatically, in this paper for the energy-generating process we have used piezoelectric sensors in the speed breakers on both the sides through which energy is stored and utilized. © 2023 IEEE.

关键词： Microcontrollers

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Classification of Hashtag's Popularity Growth Patterns Using Shape Dynamic Time Warping

Classification of Hashtag's Popularity Growth Patterns Using...

引用

International Conference on Data and Software engineering (ICODSE)

作者： Ren Hayashi Yasuhiro Hashimoto Dept. Information Technologies and Project Management The University of Aizu Aizu-Wakamatsu Japan Dept. Computer Science and Engineering The University of Aizu Aizu-Wakamatsu Japan

ISBN: (数字)9798331506407

ISBN: (纸本)9798331506414

This study presents a new method for classifying growth patterns of cultural elements, specifically focusing on hashtag popularity in social media. We use the Yule-Simon process as a baseline and apply Shape Dynamic Time Warping (ShapeDTW) to categorize hashtags into distinct growth patterns. Our approach successfully identified typical growth patterns by setting similarity thresholds. Notably, our linear and logarithmic scale comparison revealed that the latter was more effective in identifying characteristic growth patterns. This was particularly evident for time series demonstrating Preferential Attachment-like mechanisms. Our findings highlight the importance of considering both scale and underlying growth mechanisms when analyzing time series of cultural phenomena.

关键词： Social networking (online) Shape Image color analysis Shape measurement Time series analysis Focusing Time measurement Cultural differences Software engineering

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Power Saving and Power Generating in Automobile (Self-Charging Kit)

Power Saving and Power Generating in Automobile (Self-Chargi...

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2023 International Conference on Disruptive Technologies, ICDT 2023

作者： Singh, Jay Datta, Priyanka Kumar, Nagendra Sharma, Kailash Saxena, Abhinav Verma, Aditya Ambika Pathy, A. Gl Bajaj Institute of Technology & Management Eee Department Gr Noida India Jss Academy of Technical Education Dept. of Eee U.P. Noida India Graphic Era Hill University Computer Science and Engineering Dehradun India Galgotias College of Engineering and Technology Dept. of Eee Gr. Noida India

ISBN: (纸本)9798350323887

Nowadays most well Grounded and Slashed source of Energy is wind energy and it is easily within reach, convenient and easy to employ. So, as we are growing day by day with Technology, it's our responsibility to protect our nature for these Engineers and scientist are unfailingly working on different technologies to make human Life reliable and easy to access energy. So, we are also giving a little contribution to our society to make environment pollution free and help for humanities. This research paper is tiny contributions to our society in this confounding field so authors trust that in future this wondrous innovation could assist us with vanquishing all Reliable energy issues so that forthcoming Age could utilize that innovation. So here authors come with innovative solution Power Saving and Power Generating in Automobile using Wind Energy. © 2023 IEEE.

关键词： Wind power

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Smart Modular Few-Shot Learning Framework Changes Classification of Signal Modulation

Smart Modular Few-Shot Learning Framework Changes Classifica...

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2023 International Conference on Data Science and Network Security, ICDSNS 2023

作者： Spoorthi, T. Tabassum, Husna Devika, S.V. Rao, K. B. V. Brahma Ramachandra, A.C. Gst Gitam University Bengaluru Campus Dept. of Cse Bengaluru India Hkbk College of Engineering Dept. of Cse Bengaluru India Hyderabad Institute of Technology and Management Department of Ece Hyderabad India Koneru Lakshmaiah Education Foundation Department of Computer Science and Engineering Vaddeswaram India Nitte Meenakshi Institute of Technology Department of Electronics and Communication Engineering Bengaluru India

ISBN: (纸本)9798350301595

The purpose of this research is to categorise signal modulation using few-shot learning. The first approach is an intelligent, modular, few-shot learning framework. This system's two components are IQF and 1D-SFP, and it also has a task-driven network design. The 1D-SFP module is responsible for obtaining high-level characteristics from the raw signal, while the IQF module is in charge of retrieving low-level information. The number of 1D-SFP modules required for processing a large amount of data is calculated by studying the correlation in the hidden layer. We evaluate the suggested method using a publicly available dataset and show that it beats standard strategies in terms of accuracy. It can also learn from a small number of instances and is adaptable enough to handle innovative modulation schemes. This enables it to be used in wireless networks for data transmission and reception. The second approach uses a graph convolutional network (GCN) to calculate how many 1D-SFP modules should be integrated into a neural network architecture for a particular job. On benchmark datasets, the presented frameworks outperformed a range of state-of-the-art, conventional methodologies. Both approaches perform preprocessing, representation learning, and classification using their own distinct algorithmic frameworks. The presented strategies have the potential to increase signal modulation classification precision and efficiency in cognitive radios and other kinds of wireless communication. These gains are possible because of the potential of these techniques. © 2023 IEEE.

关键词： Learning systems

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