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2020 International Conference on computer, control, Electrical, and Electronics engineering, ICCCEEE 2020

作者： Eltayeb, Ahmed Rahmat, Mohd Fuaad Eltoum, M. A Mohammed Ibrahim, M. H. Sanhoury Basri, Mohd Ariffanan Mohd Universiti Teknologi Malaysia School of Electrical Engineering Faculty of Engineering Skudai Johor1310 Malaysia Kfupm University Faculty of Computer Science and Engineering System Engineering Dept Dhahran31261 Saudi Arabia Alneelain University College of Engineering Dept. of Control Engineering Khartoum Sudan

ISBN: (纸本)9781728191119

Currently, the quadcopter Unmanned Aerial Vehicles (UAVs) are playing a significant role in combating the COVID-19 pandemic crisis, which induced the researchers to design robust control techniques. In this paper, a fuzzy PID controller is designed to stabilize and/or track the desired trajectory of the quadcopter UAV. The mathematical model of the quadcopter UAV has been briefly presented, where it has been divided into two portions, the position dynamic and the attitude dynamic subsystems. Subsequently, a robust fuzzy PID controller has been designed for both the inner loop and outer loop to control and stabilize the position and the attitude of the quadcopter, which adaptively manipulate the system's input based on the tracking error. The proposed controller is benchmarked with the conventional PID controller to show the robustness of the fuzzy PID controller. Fuzzy PID controller has been verified through simulation work utilizing Matlab/Simulink, where better performance is achieved compared with the conventional PID controller. It is found that the errors in the quadcopter's attitude and position have been significantly reduced through using fuzzy PID controller by 70% and 87%, respectively. © 2021 IEEE.

关键词： Simulink

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State Space Mathematical Modeling and State of Charge Estimation of 18650 Panasonic Li-Ion Battery Using $\mathcal{L}_{2}-\mathcal{L}_{\infty}$ Observer Design

State Space Mathematical Modeling and State of Charge Estima...

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International Conference on Power Electronic Drives

作者： R.B. Ajay Kumar R. Manivannan School of Electrical and Electronics Engineering SASTRA Deemed To Be University Thanjavur Tamil Nadu India Dept. of Mathematics Artificial Intelligence and Control Systems (AI & CS) LAB SASHE SASTRA Deemed To Be University Thanjavur Tamil Nadu India

ISBN: (数字)9798350372472

ISBN: (纸本)9798350372489

This study focuses on the $\mathcal{L}_{2}-\mathcal{L}_{\infty}$ observer design, which aims to enhance the accuracy of State of Charge (SOC) estimation in a 2RC (two resistor-capacitor) Li-Ion battery (LIB) model. Validation was performed using Hybrid Pulse Power Characterization (HPPC) for Panasonic (NCR-18650B) cells. The observer's goal is to accurately estimate the battery condition despite various uncertainties and disruptions, utilizing Lyapunov methods and linear matrix inequality (LMI) techniques. It employs observer gain to reduce disturbances and $\mathcal{L}_{2}-\mathcal{L}_{\infty}$ norms to ensure minimal error in steady-state performance. Integrating this observer allows the model to more accurately capture the battery's dynamic behavior, resulting in improved state estimation and reliable insights into battery performance under various operating conditions.

关键词： Lithium-ion batteries Uncertainty Observers Mathematical models Linear matrix inequalities Steady-state State-space methods State of charge Reliability Lyapunov methods

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A Comprehensive Survey of Text Classification Techniques and Their Research Applications: Observational and Experimental Insights

arXiv

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arXiv 2024年

作者： Taha, Kamal Yoo, Paul D. Yeun, Chan Homouz, Dirar Taha, Aya Department of Computer Science Khalifa University United Arab Emirates Department of Computer Science & Information Systems University of London Birkbeck College United Kingdom Center for Cyber-Physical Systems Dept. of Electrical Engineering and Computer Science Khalifa University United Arab Emirates Department of Physics Khalifa University United Arab Emirates Brighton College Dubai United Arab Emirates

The exponential growth of textual data presents substantial challenges in management and analysis, notably due to high storage and processing costs. Text classification, a vital aspect of text mining, provides robust solutions by enabling efficient categorization and organization of text data. These techniques allow individuals, researchers, and businesses to derive meaningful patterns and insights from large volumes of text. This survey paper introduces a comprehensive taxonomy specifically designed for text classification based on research fields. The taxonomy is structured into hierarchical levels: research field-based category, research field-based sub-category, methodology-based technique, methodology sub-technique, and research field applications. We employ a dual evaluation approach: empirical and experimental. Empirically, we assess text classification techniques across four critical criteria. Experimentally, we compare and rank the methodology sub-techniques within the same methodology technique and within the same overall research field sub-category. This structured taxonomy, coupled with thorough evaluations, provides a detailed and nuanced understanding of text classification algorithms and their applications, empowering researchers to make informed decisions based on precise, field-specific insights. © 2024, CC BY-NC-SA.

关键词： Deep learning

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Learning on the edge: online learning with stochastic feedback graphs 22

Learning on the edge: online learning with stochastic feedba...

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Emmanuel Esposito Federico Fusco Dirk van der Hoeven Nicolò Cesa-Bianchi Dept. of Computer Science Università degli Studi di Milano Italy and Istituto Italiano di Tecnologia Italy Dept. of Computer Control and Management Engineering Sapienza Università di Roma Italy Dept. of Computer Science Università degli Studi di Milano Italy

ISBN: (纸本)9781713871088

The framework of feedback graphs is a generalization of sequential decisionmaking with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erdős-Rényi model. Specifically, in each round every edge in the graph is either realized or not with a distinct probability for each edge. We prove nearly optimal regret bounds of order $\min\bigl\{\min_{\varepsilon} \sqrt{(\alpha_\varepsilon/\varepsilon) T},\, \min_{\varepsilon} (\delta_\varepsilon/\varepsilon)^{1/3} T^{2/3}\bigr\}$ (ignoring logarithmic factors), where αε and δε are graph-theoretic quantities measured on the support of the stochastic feedback graph G with edge probabilities thresh-olded at ε. Our result, which holds without any preliminary knowledge about G, requires the learner to observe only the realized out-neighborhood of the chosen action. When the learner is allowed to observe the realization of the entire graph (but only the losses in the out-neighborhood of the chosen action), we derive a more efficient algorithm featuring a dependence on weighted versions of the independence and weak domination numbers that exhibits improved bounds for some special cases.

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Clustered Graph Federated Personalized Learning

Clustered Graph Federated Personalized Learning

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Asilomar Conference on Signals, systems & computers

作者： Francois Gauthier Vinay Chakravarthi Gogineni Stefan Werner Yih-Fang Huang Anthony Kuh Dept. of Electronic Systems Norwegian University of Science and Technology Norway Dept. of Electrical Engineering University of Notre Dame Notre Dame IN USA Dept. of Electrical and Computer Engineering University of Hawai'i Hawai'i USA

This paper proposes a graph federated learning approach wherein multiple servers collaborate to enhance personalized learning over clustered clients, essentially performing correlated learning tasks. In contrast to earlier approaches, relying on a cluster-dedicated server topology, the proposed graph federated multitask learning (GFedMt) framework adopts a more general setting wherein clients of the same cluster are distributed across servers. In order to address problems with unbalanced client distributions among servers and clusters as well as data shortage of isolated clients, servers perform intra-cluster and inter-cluster learning collaboratively through local interaction with neighboring servers. Clients use the alternating direction method of multipliers (ADMM) to learn their local models. Numerical simulations demonstrate the ability of the proposed method to ensure fast and accurate convergence when data is scarce.

关键词： Processor scheduling Federated learning Numerical simulation Convex functions Data models Numerical models Topology

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Scalability in Autoencoder-based OFDM Communication System

Scalability in Autoencoder-based OFDM Communication System

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computer Vision and Machine Intelligence (CVMI), International Conference on

作者： Seizan Tsugawa Takao Toma Shiho Oshiro Tomohisa Wada Computer Science and Intelligent Systems Program University of the Ryukyus Okinawa Japan Magna Design Net Inc Okinawa Japan Information Technology Center University of the Ryukyus Okinawa Japan Dept. of Engineering Area of Computer Science and Intelligent Systems University of the Ryukyus Okinawa Japan

This paper proposed Scalability in Autoencoder-based Orthogonal Frequency Division Multiplexing(OFDM) communication system. In the previous research, only the comparison between IEEE802.11a and Autoencoder by the conventional OFDM communication system was performed, and it was proved that the communication system created by Autoencoder exceeded the performance of the conventional system. Therefore, in this paper, it uses IEEE802.11n and compare whether it can be improved by expanding the bandwidth and using ***802.11n standard has an FFT length of 128, a subcarrier number of 114 (108 for data), and modulation schemes of Quadrature Phase Shift Keying(QPSK), 16 Quadrature Amplitude Modulation (16QAM). The GI length is 800ns and the symbol length is 4000ns. In the simulation, a computer simulation was performed using a conventional OFDM communication system and a communication system generated by Autoencoder. Assuming that the simulation environment had an Signal-to-Noise Ratio (SNR) of 0 to 30 and an amplitude r of 0.0 to 1.0, the Symbol Error Rate (SER) status for each SNR was output. As a result of computer simulation, QPSK converged at SNR 27 ㏈ at IEEE 802.11a, but was able to reduce SER overwhelmingly to SNR 12 ㏈ at IEEE 802.11n. Also, in 16QAM the convergence at r=0.0 is the same as for SNR22 ㏈, but in IEEE 802.11a it does not converge after r=0.6, but in IEEE 802.11n it does not converge only at r=0.9 and r=1.0 . As a future task, it will use IEEE802.11ac, which enables communication speeds several times faster than IEEE802.11n, examine whether it is possible to further improve accuracy. And it will continue our research to correspond MIMO communication.

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Blockchain Integration for Robust Intellectual Property Protection in New Product Development (NPD)

Blockchain Integration for Robust Intellectual Property Prot...

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Electronics and Renewable systems (ICEARS), International Conference on

作者： P. Chinnasamy Subashini. B Nijanthan Nijanthan Madasamy Raja. G Devulapally Swetha Sreenivasulu Reddy L Department of Computer Science and Engineering School of Computing Kalasalingam Academy of Research and Education Tamilnadu India Department of Data Science and Business Systems School of Computing SRMIST India Computer Science and Engineering Paavai Engineering College Namakkal Tamilnadu India Information Technology Paavai Engineering College Namakkal Tamilnadu India Dept. of Computer Science and Engineering Vaagdevi Engineering College Telangana India Department of Mathematics School of Advanced Sciences Kalasalingam Academy of Research and Education Krishnankoil Tamilnadu India

ISBN: (数字)9798331509675

ISBN: (纸本)9798331509682

The rapid advancement of Blockchain technology has led to its widespread application in several domains of digital activities, such as e-government concerns and monetary security. This article presents a smart contract-based Blockchain solution for protecting intellectual property rights in new product development. The proposed solution effectively tackles the challenges associated with the confidentiality of personally identifiable information, efficiency in storing transaction-related information, as well as cooperation and monitoring in IPR. Initially, we create a mathematical model based on smart contract enabled Blockchain and intelligent contracts. Subsequently, we develop algorithms for Blockchain generation, and intelligent contracts. The system is suitable for protecting IPR in a blockchain network, by implementation of distributed identity authentication and real-time extensible storage. These features enhance the security and expandability of circuit copyright protection based on blockchain technology. The experimental results demonstrate that the suggested algorithm has decreased the cost for communication and enhanced the effectiveness of storing information and administration. Furthermore, it demonstrates resilience against many common attacks such as double-spending assaults, while maintaining low cost and overhead. Additionally, it exhibits a superior level of security in comparison to three other competing algorithms.

关键词： Patents Technological innovation Costs Smart contracts Intellectual property Product development Blockchains Security Protection Resilience

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A Demonstrative Study on Prediction of Disease Using Bayesian Machine Learning Techniques

A Demonstrative Study on Prediction of Disease Using Bayesia...

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IEEE International Conference on System, Computation, Automation and Networking (ICSCAN)

作者： R. Dhanalakshmi N. Vijayaraghavan S. Narasimhan R Rajesh Centre for Cyber Physical Systems School. of Computer Science and Engineering Vellore Institute of Technology Chennai India Dept. of Mathematics KCG College of Technology Chennai India School. of Computer Science and Engineering Vellore Institute of Technology Chennai India

With the rapid advancement of biological information, accurate analysis of treatment data aids in early disease detection. To uncover knowledge for medical research, advanced Machine Learning algorithms are applied. Here in this research paper, to determine if a patient is affected by any Infectious disorders, we use the Naive Bayes Algorithm method.

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Learning on the Edge: Online Learning with Stochastic Feedback Graphs

arXiv

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arXiv 2022年

作者： Esposito, Emmanuel Fusco, Federico van der Hoeven, Dirk Cesa-Bianchi, Nicolò Dept. of Computer Science Università degli Studi di Milano Italy Istituto Italiano di Tecnologia Italy Dept. of Computer Control and Management Engineering Sapienza Università di. Roma Italy

The framework of feedback graphs is a generalization of sequential decision-making with bandit or full information feedback. In this work, we study an extension where the directed feedback graph is stochastic, following a distribution similar to the classical Erdős-Rényi model. Specifically, in each round every edge in the graph is either realized or not with a distinct probability for each edge. We prove nearly optimal regret bounds of order min(Fomula Presented)(ignoring logarithmic factors), where αΕ and δΕ are graph-theoretic quantities measured on the support of the stochastic feedback graph G with edge probabilities thresholded at Ε. Our result, which holds without any preliminary knowledge about G, requires the learner to observe only the realized out-neighborhood of the chosen action. When the learner is allowed to observe the realization of the entire graph (but only the losses in the out-neighborhood of the chosen action), we derive a more efficient algorithm featuring a dependence on weighted versions of the independence and weak domination numbers that exhibits improved bounds for some special cases. Copyright © 2022, The Authors. All rights reserved.

关键词： Stochastic systems

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An Approach for Improving Automatic Mouth Emotion Recognition

arXiv

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arXiv 2022年

作者： Biondi, Giulio Franzoni, Valentina Gervasi, Osvaldo Perri, Damiano University of Florence Dept. of Mathematics and Computer Science Florence Italy University of Perugia Dept. of Mathematics and Computer Science Perugia Italy Sapienza University of Rome Department of Computer Control and Management Engineering "Antonio Ruberti" Rome Italy

The study proposes and tests a technique for automated emotion recognition through mouth detection via Convolutional Neural Networks (CNN), meant to be applied for supporting people with health disorders with communication skills issues (e.g. muscle wasting, stroke, autism, or, more simply, pain) in order to recognize emotions and generate real-time feedback, or data feeding supporting systems. The software system starts the computation identifying if a face is present on the acquired image, then it looks for the mouth location and extracts the corresponding features. Both tasks are carried out using Haar Feature-based Classifiers, which guarantee fast execution and promising performance. If our previous works focused on visual micro-expressions for personalized training on a single user, this strategy aims to train the system also on generalized faces data sets. © 2022, CC BY-NC-ND.

关键词： Emotion Recognition

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