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Artificial Neural Network-based Simplified Model Predictive Control for Enhancing Voltage Source Inverters: CMV Reduction and Weighting Factor Optimization 50

Artificial Neural Network-based Simplified Model Predictive ...

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50th Annual Conference of the IEEE Industrial Electronics Society, IECON 2024

作者： Shokrani, Mohammad Imanlou, Arash Behkam, Reza Nazari-Heris, Morteza Department of Electrical and Computer Engineering University of Tabriz Tabriz Iran Department of Electrical Engineering Amirkabir University of Technology Tehran Polytechnic Tehran Iran Department of Engineering East Carolina University GreenvilleNC United States

ISBN: (纸本)9781665464543

Controlling voltage source inverters (VSIs) is crucial for ensuring the efficient operation of inverter-based systems. Model predictive control (MPC), notably finite control set MPC (FCS-MPC), is increasingly recognized as an effective control strategy within power electronics, particularly for addressing multi-objective control problems. This article proposes a novel approach for controlling voltage source inverters using a simplified FCS-MPC scheme with common-mode voltage (CMV) reduction, limited switching frequency, and optimized weighting factors (WFs). The proposed method simplifies the MPC controller by replacing the computational burden of current predictions with a single voltage reference estimation, thereby reducing computational complexity. Moreover, offline optimization based on the non-dominated sorting genetic algorithm II (NSGA-II) is employed to determine optimized weighting factors for the cost function, as well as enhance the controller's performance. For further alleviation of computational burden, the controller is imitated using an artificial neural network (ANN) trained offline with data labeled by the original FCS-MPC algorithm. A simulation study is presented to control both conventional two-level VSI (2L-VSI) and three-level neutral point clamped (3L-NPC) inverters to demonstrate the effectiveness and validity of the proposed method. The results underline that the ANN imitator of simplified MPC reduces computational complexity while maintaining performance comparable to optimal control. © 2024 IEEE.

关键词： Cost functions

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Study on Real-Time Heart Rate Detection Based on Multi-People

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computer Systems Science & engineering 2023年第2期44卷 1397-1408页

作者： Qiuyu Hu Wu Zeng Yi Sheng Jian Xu Weihua Ou Ruochen Tan School of Electrical and Electronic Engineering Wuhan Polytechnic UniversityWuhan430000China School of Electronic and Information Engineering Guizhou Normal UniversityGuizhou550000China Interactive Media and Computer Science University of MiamiMiami33124FloridaUSA

Heart rate is an important vital characteristic which indicates physical and mental health *** heart rate measurement instruments require direct contact with the skin which is time-consuming and ***,the study of non-contact heart rate measurement methods is of great *** on the principles of photoelectric volumetric tracing,we use a computer device and camera to capture facial images,accurately detect face regions,and to detect multiple facial images using a multi-target tracking *** after the regional segmentation of the facial image,the signal acquisition of the region of interest is further ***,frequency detection of the collected Photo-plethysmography(PPG)and Electrocardiography(ECG)signals is completed with peak detection,Fourier analysis,and a Waveletﬁ*** experimental results show that the subject’s heart rate can be detected quickly and accurately even when monitoring multiple facial targets simultaneously.

关键词： Face recognition face analysis heart rate detection IPPG signal

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Analysis of Blockchain Sustainability through the Comparison of Different Smart Contracts Programming Languages 6

Analysis of Blockchain Sustainability through the Comparison...

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6th Distributed Ledger Technology Workshop, DLT 2024

作者： Bistarelli, Stefano Fiore, Marco Lazzizera, Antonio Ignazio Mercanti, Ivan Mongiello, Marina Department of Mathematics and Computer Science University of Perugia Perugia Italy Department of Electrical and Information Engineering Polytechnic University of Bari Bari Italy

This paper aims to analyze the correlation between programming languages used to implement smart contracts and the related Blockchain platforms, seeking an economically and environmentally viable solution. The research selects four programming languages commonly employed for smart contract creation, defining three standard functions that will be tested on the Ethereum, Tezos, Solana, and Polkadot Blockchains. The study investigates how these programming languages directly impact the amount of energy resources required and subsequently influence the cost of executing a smart contract. © 2024 Copyright for this paper by its authors.

关键词： Smart contract

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computer Science Education Facing Unconventional Odds: Case Studies from the Arab World

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ACM Inroads 2024年第1期15卷 6-17页

作者： Aly, Sherif G. Echihabi, Karima Eldawlatly, Seif Shuaib, Khaled Tekli, Joe Department of Computer Science and Engineering The American University in Cairo Cairo Egypt Department of Computer Science Mohamed VI Polytechnic University Ben Guerir Morocco Department of Computer Science and Engineering The American University in Cairo AUC Avenue New Cairo Cairo11835 Egypt Ain Shams University Cairo Egypt College of Information Technology United Arab Emirates University Abu Dhabi United Arab Emirates Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon

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Enhanced LSTM by Attention Mechanism for Early Detection of Parkinson's Disease Through Voice Signals 2

Enhanced LSTM by Attention Mechanism for Early Detection of ...

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2nd International engineering Conference on electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Mohammadigilani, Arman Attar, Hani Chimeh, Hamidreza Ehsani Karami, Mostafa Polytechnic University of Turin Electrical and Computer Engineering Turin Italy Zarqa University Faculty of Engineering Zarqa Jordan College of Business Administration University of Business and Technology Jeddah21448 Saudi Arabia University of Newfoundland Electronics and Telecommunications Memorial St. John's Canada

ISBN: (纸本)9798350373363

Parkinson's disease (PD) is a neurodegenerative condition characterized by notable motor and non-motor manifestations. The assessment tool known as the Unified Parkinson's Disease Rating Scale (UPDRS) plays a crucial role in evaluating the extent of symptomatology associated with Parkinson's Disease (PD). This research presents a complete approach for predicting UPDRS scores using sophisticated Long Short-Term Memory (LSTM) networks that are improved using attention mechanisms, data augmentation techniques, and robust feature selection. The data utilized in this work was obtained from the UC Irvine Machine Learning repository. It encompasses a range of speech metrics collected from patients in the early stages of Parkinson's disease. Recursive Feature Elimination (RFE) was utilized to achieve efficient feature selection, while the application of jittering enhanced the dataset. The Long Short-Term Memory (LSTM) network was carefully crafted to capture temporal fluctuations within the dataset effectively. Additionally, it was enhanced by integrating an attention mechanism, which enhances the network's ability to recognize sequence importance. The methodology that has been described presents a potentially practical approach for conducting a more precise and individualized analysis of medical data related to Parkinson's disease. © 2023 IEEE.

关键词： Long short-term memory

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Nonlinear Optimal Regulation of SIR Epidemic Model via Finite-Horizon SDRE 26

Nonlinear Optimal Regulation of SIR Epidemic Model via Finit...

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26th International Conference on computer and Information Technology, ICCIT 2023

作者： Paul, Sudipta Islam, Samiul Naidu, Desineni Subbaram Abu Hanif Pramanik, Md. Rensselaer Polytechnic Institute Department of Electrical Computer & Systems Engineering New York United States Rajshahi University of Engineering & Technology Department of Electrical & Computer Engineering Bangladesh University of Minnesota Duluth Department of Electrical Engineering United States

ISBN: (纸本)9798350359015

A Susceptible-Infectious-Recovered (SIR) model is a popular and fundamental epidemiological model often used to assess the efficacy of disease prevention and control measures. SIR disease model, with the implementation of various optimal control methods, is investigated to determine the optimal ways to lessen the effects of an infectious disease. In this research work, a non-linear, closed-loop, finite-horizon, optimal regulation with state-dependent Riccati equation (FH-SDRE) is implemented in an epidemic model called SIR model with vaccination to minimize the infected and susceptible population. Given the nonlinear dynamics of the SIR epidemic model, initially, this method converts the nonlinear equations into a state-dependent coefficient (SDC) matrix, which holds the nonlinearities of the dynamical system. After that, SDRE is solved at every small time instant over a specified finite time period. The core idea here is to utilize an approximate analytical approach that transforms the differential Riccati equation (DRE) into a differential Lyapunov equation (DLE) during every time period. Then, the real-time solution of the DLE at each of these time steps will result in the control law required to achieve the desired system performance. The effectiveness of the FH-SDRE regulation for the SIR model is demonstrated by minimizing the infected and susceptible population within a desired 30-day time period. © 2023 IEEE.

关键词： finite-horizon regulation SIR epidemic model state-dependent Riccati equation

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TMD-Tradeoff and Slide Attack Analysis on the Novel ZUC-PRN Model 18

TMD-Tradeoff and Slide Attack Analysis on the Novel ZUC-PRN ...

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18th IEEE International Conference on Advanced Networks and Telecommunications Systems, ANTS 2024

作者： Dutta, Jayati Peri, Priyanka Malkuchi, Rohith Indian Institute of Technology Electrical Engineering Hyderabad India Virginia Polytechnic Institute and State University Electronics and Computer Engineering Blacksburg United States

ISBN: (纸本)9798350391725

The rapid evolution of wireless communication technologies is epitomized by the advent of 5G-Advanced and the forthcoming 6G era. While 5G-Advanced enhances existing capabilities, 6G aims to seamlessly integrate AI-driven intelligence and sophisticated cryptographic techniques, significantly bolstering security. This paper explores the ZUC-PRN model [1], a neural network-based cipher inspired by the ZUC algorithm, proposed for enhancing wireless security in 5G and 6G networks. While ZUC-PRN shows potential for replacing traditional FSM-based ciphers, its resilience against various attacks remains unverified. We analyze its vulnerabilities to Slide and Time-Memory-Data Tradeoff (TMDT) attacks and assess its computational complexity, comparing it with the standard ZUC stream cipher. This study provides critical insights into the security and efficiency of the ZUC-PRN model, contributing to the development of more robust encryption methods for next-generation communication systems. © 2024 IEEE.

关键词： 5G mobile communication systems

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Accurate and efficient remaining useful life prediction of batteries enabled by physics-informed machine learning

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Journal of Energy Chemistry 2024年第4期91卷 512-521页

作者： Liang Ma Jinpeng Tian Tieling Zhang Qinghua Guo Chunsheng Hu School of Mechanical MaterialsMechatronic and Biomedical EngineeringUniversity of WollongongWollongongNSW 2522Australia Department of Electrical and Electronic Engineering and Research Centre for Grid Modernization The Hong Kong Polytechnic UniversityKowloonHong Kong 999077China School of Electrical Computer and Telecommunications EngineeringUniversity of WollongongWollongongNSW 2522Australia School of Advanced Interdisciplinary Studies Ningxia UniversityYinchuan 750000NingxiaChina

The safe and reliable operation of lithium-ion batteries necessitates the accurate prediction of remaining useful life(RUL).However,this task is challenging due to the diverse ageing mechanisms,various operating conditions,and limited measured *** data-driven methods are perceived as a promising solution,they ignore intrinsic battery physics,leading to compromised accuracy,low efficiency,and low *** response,this study integrates domain knowledge into deep learning to enhance the RUL prediction *** demonstrate accurate RUL prediction using only a single charging ***,a generalisable physics-based model is developed to extract ageing-correlated parameters that can describe and explain battery degradation from battery charging *** parameters inform a deep neural network(DNN)to predict RUL with high accuracy and *** trained model is validated under 3 types of batteries working under 7 conditions,considering fully charged and partially charged *** data from one cycle only,the proposed method achieves a root mean squared error(RMSE)of 11.42 cycles and a mean absolute relative error(MARE)of 3.19%on average,which are over45%and 44%lower compared to the two state-of-the-art data-driven methods,*** its accuracy,the proposed method also outperforms existing methods in terms of efficiency,input burden,and *** inherent relationship between the model parameters and the battery degradation mechanism is further revealed,substantiating the intrinsic superiority of the proposed method.

关键词： Lithium-ion batteries Remaining useful life Physics-informed machine learning

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Distributed Big Data Clustering using MapReduce-based Fuzzy C-Medoids

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Journal of The Institution of Engineers (India): Series B 2022年第1期103卷 73-82页

作者： Sardar, Tanvir H. Ansari, Zahid School of Computer Science & Engineering Jain University Bangalore India Electrical Engineering Section University Polytechnic Aligarh Muslim University Aligarh India

Efficient big data clustering is a requirement for massive data generating in this digitalized connected world. The traditional clustering algorithms do not scale over massively sized and highly unstructured big data. Thus, to obtain efficiency in clustering big data new architecture and programming paradigm is required. In this work, a novel MapReduce-based Fuzzy C-Medoids clustering algorithm is designed and experimented with to cluster big data repository of documents datasets. The performance of the proposed algorithm is experimentally evaluated for different-sized Hadoop cluster sizes and different-sized document datasets. The algorithm is found to be scalable and efficient in performing clustering jobs. © 2021, The Institution of Engineers (India).

关键词： MapReduce

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MapReduce-based Fuzzy C-means Algorithm for Distributed Document Clustering

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Journal of The Institution of Engineers (India): Series B 2022年第1期103卷 131-142页

The clustering of big data is a challenging task. The traditional clustering algorithms are inefficient for clustering big data. The recent researches in this field suggest that the traditional clustering algorithms needed to be redesigned for the modern architecture of computing. This wok has proposed a novel MapReduce-based fuzzy C-means algorithm for big document data clustering. The algorithm is extensively experimented with using different sizes of document datasets and executed over the Hadoop cluster of different sizes. The proposed algorithm’s efficiency is compared against serial traditional fuzzy C-means and MapReduce-based K-means algorithms. The proposed design of the fuzzy C-means algorithm is scaled well with the Hadoop platform and documents big datasets and resulted in a performance gain. © 2021, The Institution of Engineers (India).

关键词： MapReduce

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